{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import absolute_import, division, print_function, unicode_literals\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import metrics \n",
    "from tensorflow.keras.layers import Layer,Add, add,AveragePooling2D\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Conv2D, Flatten, Dropout, MaxPooling2D ,Lambda ,GlobalAveragePooling2D,DepthwiseConv2D\n",
    "from tensorflow.keras.layers import concatenate, Add, BatchNormalization, Activation, PReLU, LeakyReLU ,Reshape,Multiply, multiply\n",
    "from tensorflow.keras import regularizers , Input ,Model\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "import os\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from scipy import ndimage , misc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 128\n",
    "epochs = 20\n",
    "IMG_HEIGHT = 32\n",
    "IMG_WIDTH = 32\n",
    "QP = \"QP22\"\n",
    "train_dir = \"D:/VCCResearch/\"+QP+\"/dependency_data\"\n",
    "valid_dir = \"D:/VCCResearch/\"+QP+\"/valid_depthall\"\n",
    "#QT_train_dir = \"D:/VCCResearch/\"+QP+\"/dependency_QT78\"\n",
    "TT_train_dir = \"D:/VCCResearch/\"+QP+\"/TT_CU/train_TT\"\n",
    "TT_valid_dir = \"D:/VCCResearch/\"+QP+\"/TT_CU/valid_TT\"\n",
    "\n",
    "QT_train_dir = \"D:/VCCResearch/\"+QP+\"/train_QT_24\"\n",
    "QT_valid_dir = \"D:/VCCResearch/\"+QP+\"/valid_QT_24\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TT OR NOT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 491,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Have TT: 27235\n",
      "No TT: 35469\n",
      "===================================================\n",
      "Have TT: 0.4343\n",
      "No TT: 0.5657\n"
     ]
    }
   ],
   "source": [
    "\n",
    "train_have_TT_dir = os.path.join(TT_train_dir, 'have_tt')\n",
    "train_no_TT_dir = os.path.join(TT_train_dir, 'no_tt')\n",
    "\n",
    "num_have_TT_tr = len(os.listdir(train_have_TT_dir))\n",
    "num_no_TT_tr = len(os.listdir(train_no_TT_dir))\n",
    "\n",
    "print(\"Have TT: %d\"%num_have_TT_tr)\n",
    "print(\"No TT: %d\"%num_no_TT_tr)\n",
    "\n",
    "total_train_TT = num_have_TT_tr+num_no_TT_tr\n",
    "\n",
    "print(\"===================================================\")\n",
    "\n",
    "print(\"Have TT: %.4f\"%(num_have_TT_tr/total_train_TT))\n",
    "print(\"No TT: %.4f\"%(num_no_TT_tr/total_train_TT))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 492,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Have TT: 55126\n",
      "No TT: 70282\n",
      "===================================================\n",
      "Have TT: 0.4396\n",
      "No TT: 0.5604\n"
     ]
    }
   ],
   "source": [
    "\n",
    "valid_have_TT_dir = os.path.join(TT_valid_dir, 'have_tt')\n",
    "valid_no_TT_dir = os.path.join(TT_valid_dir, 'no_tt')\n",
    "\n",
    "num_have_TT_val = len(os.listdir(valid_have_TT_dir))\n",
    "num_no_TT_val = len(os.listdir(valid_no_TT_dir))\n",
    "\n",
    "print(\"Have TT: %d\"%num_have_TT_val)\n",
    "print(\"No TT: %d\"%num_no_TT_val)\n",
    "\n",
    "\n",
    "total_valid_TT = num_have_TT_val+num_no_TT_val\n",
    "\n",
    "print(\"===================================================\")\n",
    "\n",
    "print(\"Have TT: %.4f\"%(num_have_TT_val/total_valid_TT))\n",
    "print(\"No TT: %.4f\"%(num_no_TT_val/total_valid_TT))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 493,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "62704\n",
      "125408\n"
     ]
    }
   ],
   "source": [
    "print(total_train_TT)\n",
    "print(total_valid_TT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 494,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 62704 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "TT_train_image_generator = ImageDataGenerator(rescale = 1./255)\n",
    "TT_train_data_gen = TT_train_image_generator.flow_from_directory(batch_size=batch_size,\n",
    "                                                           directory=TT_train_dir,\n",
    "                                                           shuffle=True,\n",
    "                                                           target_size=(IMG_HEIGHT, IMG_WIDTH),\n",
    "                                                           class_mode='categorical')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 495,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 125408 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "TT_validation_image_generator = ImageDataGenerator(rescale = 1./255)\n",
    "TT_val_data_gen = TT_validation_image_generator.flow_from_directory(batch_size=batch_size,\n",
    "                                                              directory=TT_valid_dir,\n",
    "                                                              target_size=(IMG_HEIGHT, IMG_WIDTH),\n",
    "                                                              shuffle=False,\n",
    "                                                              class_mode='categorical')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# QT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 496,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "QT2: 43212\n",
      "QT3: 79813\n",
      "===================================================\n",
      "QT2: 0.3512\n",
      "QT3: 0.6488\n"
     ]
    }
   ],
   "source": [
    "\n",
    "train_QT2_dir = os.path.join(QT_train_dir, 'QT1')\n",
    "train_QT3_dir = os.path.join(QT_train_dir, 'QT2')\n",
    "\n",
    "num_QT2_tr = len(os.listdir(train_QT2_dir))\n",
    "num_QT3_tr = len(os.listdir(train_QT3_dir))\n",
    "\n",
    "\n",
    "\n",
    "print(\"QT2: %d\"%num_QT2_tr)\n",
    "print(\"QT3: %d\"%num_QT3_tr)\n",
    "\n",
    "total_train_QT = num_QT2_tr+num_QT3_tr\n",
    "\n",
    "print(\"===================================================\")\n",
    "\n",
    "print(\"QT2: %.4f\"%(num_QT2_tr/total_train_QT))\n",
    "print(\"QT3: %.4f\"%(num_QT3_tr/total_train_QT))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 497,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "QT2: 18316\n",
      "QT3: 37083\n",
      "===================================================\n",
      "QT2: 0.3306\n",
      "QT3: 0.6694\n"
     ]
    }
   ],
   "source": [
    "\n",
    "valid_QT2_dir = os.path.join(QT_valid_dir, 'QT1')\n",
    "valid_QT3_dir = os.path.join(QT_valid_dir, 'QT2')\n",
    "\n",
    "num_QT2_valid = len(os.listdir(valid_QT2_dir))\n",
    "num_QT3_valid = len(os.listdir(valid_QT3_dir))\n",
    "\n",
    "\n",
    "print(\"QT2: %d\"%num_QT2_valid)\n",
    "print(\"QT3: %d\"%num_QT3_valid)\n",
    "\n",
    "total_valid_QT = num_QT2_valid+num_QT3_valid\n",
    "\n",
    "\n",
    "print(\"===================================================\")\n",
    "print(\"QT2: %.4f\"%(num_QT2_valid/total_valid_QT))\n",
    "print(\"QT3: %.4f\"%(num_QT3_valid/total_valid_QT))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 498,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "123025\n",
      "55399\n"
     ]
    }
   ],
   "source": [
    "print(total_train_QT)\n",
    "print(total_valid_QT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 499,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 123025 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "QT_train_image_generator = ImageDataGenerator(rescale = 1./255)\n",
    "QT_train_data_gen = QT_train_image_generator.flow_from_directory(batch_size=batch_size,\n",
    "                                                           directory=QT_train_dir,\n",
    "                                                           shuffle=True,\n",
    "                                                           target_size=(IMG_HEIGHT, IMG_WIDTH),\n",
    "                                                           class_mode='categorical')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 500,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 55399 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "QT_validation_image_generator = ImageDataGenerator(rescale = 1./255)\n",
    "QT_val_data_gen = QT_validation_image_generator.flow_from_directory(batch_size=batch_size,\n",
    "                                                              directory=QT_valid_dir,\n",
    "                                                              target_size=(IMG_HEIGHT, IMG_WIDTH),\n",
    "                                                              shuffle=False,\n",
    "                                                              class_mode='categorical')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# QT+BT+TT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "depth5_6: 25056\n",
      "depth7_8: 58761\n",
      "depth9_10: 29184\n",
      "===================================================\n",
      "depth5_6: 0.222\n",
      "depth7_8: 0.520\n",
      "depth9_10: 0.258\n"
     ]
    }
   ],
   "source": [
    "\n",
    "train_depth5_6_dir = os.path.join(train_dir, 'depth5_6')\n",
    "train_depth7_8_dir = os.path.join(train_dir, 'depth7_8')\n",
    "train_depth9_10_dir = os.path.join(train_dir, 'depth9_10')\n",
    "\n",
    "\n",
    "num_depth5_6_tr = len(os.listdir(train_depth5_6_dir))\n",
    "num_depth7_8_tr = len(os.listdir(train_depth7_8_dir))\n",
    "num_depth9_10_tr = len(os.listdir(train_depth9_10_dir))\n",
    "\n",
    "\n",
    "print(\"depth5_6: %d\"%num_depth5_6_tr)\n",
    "print(\"depth7_8: %d\"%num_depth7_8_tr)\n",
    "print(\"depth9_10: %d\"%num_depth9_10_tr)\n",
    "\n",
    "\n",
    "total_train = num_depth5_6_tr+num_depth7_8_tr+num_depth9_10_tr\n",
    "\n",
    "print(\"===================================================\")\n",
    "\n",
    "print(\"depth5_6: %.3f\"%(num_depth5_6_tr/total_train))\n",
    "print(\"depth7_8: %.3f\"%(num_depth7_8_tr/total_train))\n",
    "print(\"depth9_10: %.3f\"%(num_depth9_10_tr/total_train))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16743\n",
      "38440\n",
      "31569\n",
      "===================================================\n",
      "depth5_6: 0.193\n",
      "depth7_8: 0.443\n",
      "depth9_10: 0.364\n"
     ]
    }
   ],
   "source": [
    "\n",
    "valid_depth5_6_dir = os.path.join(valid_dir, 'depth5_6')\n",
    "valid_depth7_8_dir = os.path.join(valid_dir, 'depth7_8')\n",
    "valid_depth9_10_dir = os.path.join(valid_dir, 'depth9_10')\n",
    "\n",
    "\n",
    "num_depth5_6_valid = len(os.listdir(valid_depth5_6_dir))\n",
    "num_depth7_8_valid = len(os.listdir(valid_depth7_8_dir))\n",
    "num_depth9_10_valid = len(os.listdir(valid_depth9_10_dir))\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "print(num_depth5_6_valid)\n",
    "print(num_depth7_8_valid)\n",
    "print(num_depth9_10_valid)\n",
    "\n",
    "\n",
    "\n",
    "total_val = num_depth5_6_valid+num_depth7_8_valid+num_depth9_10_valid\n",
    "\n",
    "print(\"===================================================\")\n",
    "\n",
    "print(\"depth5_6: %.3f\"%(num_depth5_6_valid/total_val))\n",
    "print(\"depth7_8: %.3f\"%(num_depth7_8_valid/total_val))\n",
    "print(\"depth9_10: %.3f\"%(num_depth9_10_valid/total_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "113001\n",
      "86752\n"
     ]
    }
   ],
   "source": [
    "print(total_train)\n",
    "print(total_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 113001 images belonging to 3 classes.\n"
     ]
    }
   ],
   "source": [
    "train_image_generator = ImageDataGenerator(rescale = 1./255)\n",
    "\n",
    "train_data_gen = train_image_generator.flow_from_directory(batch_size=batch_size,\n",
    "                                                           directory=train_dir,\n",
    "                                                           shuffle=True,\n",
    "                                                           target_size=(IMG_HEIGHT, IMG_WIDTH),\n",
    "                                                           class_mode='categorical')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 86752 images belonging to 3 classes.\n"
     ]
    }
   ],
   "source": [
    "\n",
    "validation_image_generator = ImageDataGenerator(rescale = 1./255)\n",
    "\n",
    "val_data_gen = validation_image_generator.flow_from_directory(batch_size=batch_size,\n",
    "                                                              directory=valid_dir,\n",
    "                                                              target_size=(IMG_HEIGHT, IMG_WIDTH),\n",
    "                                                              shuffle=False,\n",
    "                                                              class_mode='categorical')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(128, 32, 32, 3)\n",
      "tf.Tensor(\n",
      "[[[0.6431373 ]\n",
      "  [0.63921577]\n",
      "  [0.7294118 ]\n",
      "  ...\n",
      "  [0.5803922 ]\n",
      "  [0.4431373 ]\n",
      "  [0.5294118 ]]\n",
      "\n",
      " [[0.67058825]\n",
      "  [0.6627451 ]\n",
      "  [0.7137255 ]\n",
      "  ...\n",
      "  [0.6       ]\n",
      "  [0.5019608 ]\n",
      "  [0.46274513]]\n",
      "\n",
      " [[0.69803923]\n",
      "  [0.6901961 ]\n",
      "  [0.72156864]\n",
      "  ...\n",
      "  [0.5686275 ]\n",
      "  [0.58431375]\n",
      "  [0.45098042]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[0.6117647 ]\n",
      "  [0.6745098 ]\n",
      "  [0.6509804 ]\n",
      "  ...\n",
      "  [0.7137255 ]\n",
      "  [0.50980395]\n",
      "  [0.50980395]]\n",
      "\n",
      " [[0.59607846]\n",
      "  [0.7058824 ]\n",
      "  [0.60784316]\n",
      "  ...\n",
      "  [0.74509805]\n",
      "  [0.5568628 ]\n",
      "  [0.6156863 ]]\n",
      "\n",
      " [[0.654902  ]\n",
      "  [0.7294118 ]\n",
      "  [0.54901963]\n",
      "  ...\n",
      "  [0.7254902 ]\n",
      "  [0.69803923]\n",
      "  [0.6117647 ]]], shape=(32, 32, 1), dtype=float32)\n",
      "=====\n",
      "[[0.6431373  0.6392157  0.7294118  ... 0.5803922  0.4431373  0.5294118 ]\n",
      " [0.67058825 0.6627451  0.7137255  ... 0.6        0.5019608  0.46274513]\n",
      " [0.69803923 0.6901961  0.72156864 ... 0.5686275  0.58431375 0.45098042]\n",
      " ...\n",
      " [0.6117647  0.6745098  0.6509804  ... 0.7137255  0.50980395 0.50980395]\n",
      " [0.59607846 0.7058824  0.60784316 ... 0.74509805 0.5568628  0.6156863 ]\n",
      " [0.654902   0.7294118  0.54901963 ... 0.7254902  0.69803923 0.6117647 ]]\n",
      "=====\n",
      "[[0.6431373  0.6392157  0.7294118  ... 0.5803922  0.4431373  0.5294118 ]\n",
      " [0.67058825 0.6627451  0.7137255  ... 0.6        0.5019608  0.46274513]\n",
      " [0.69803923 0.6901961  0.72156864 ... 0.5686275  0.58431375 0.45098042]\n",
      " ...\n",
      " [0.6117647  0.6745098  0.6509804  ... 0.7137255  0.50980395 0.50980395]\n",
      " [0.59607846 0.7058824  0.60784316 ... 0.74509805 0.5568628  0.6156863 ]\n",
      " [0.654902   0.7294118  0.54901963 ... 0.7254902  0.69803923 0.6117647 ]]\n"
     ]
    }
   ],
   "source": [
    "sample_training_images, tr_label = next(train_data_gen)\n",
    "print(sample_training_images.shape)\n",
    "print(tf.image.rgb_to_yuv(sample_training_images[0])[:,:,0:1])\n",
    "print(\"=====\")\n",
    "print(sample_training_images[0][:,:,1])\n",
    "print(\"=====\")\n",
    "print(sample_training_images[0][:,:,2])\n",
    "#print(tf.image.rgb_to_yuv(sample_training_images[0])[:,:,0:1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotImages(images_arr):\n",
    "    fig, axes = plt.subplots(8, 16, figsize=(32,32))\n",
    "    axes = axes.flatten()\n",
    "    for img, ax in zip( images_arr, axes):\n",
    "        ax.imshow(img)\n",
    "        ax.axis('off')\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2304x2304 with 128 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotImages(sample_training_images[:128])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Swish(tf.keras.layers.Layer):\n",
    "\n",
    "    def __init__(self, **kwargs):\n",
    "        super(Swish, self).__init__(**kwargs)\n",
    "        self.supports_masking = True\n",
    "\n",
    "    def call(self, inputs, training=None):\n",
    "        return tf.nn.swish(inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ResnetIdentityBlock(tf.keras.Model):\n",
    "    def __init__(self, kernel_size, filters):\n",
    "        super(ResnetIdentityBlock, self).__init__(name='')\n",
    "        self.filters1, self.filters2, self.filters3 = filters\n",
    "\n",
    "        self.conv2a = Conv2D(self.filters1, (3, 3),padding = 'same')\n",
    "        self.bn2a = BatchNormalization()\n",
    "\n",
    "        self.conv2b = Conv2D(self.filters2, (3, 3), padding='same')\n",
    "        self.bn2b = BatchNormalization()\n",
    "\n",
    "        self.conv2c = Conv2D(self.filters3, (3, 3),padding = 'same')\n",
    "        self.bn2c = BatchNormalization()\n",
    "        \n",
    "        self.gap = GlobalAveragePooling2D()\n",
    "        self.d1  = Dense(self.filters3 // 4)\n",
    "        self.d2  = Dense(self.filters3 )\n",
    "'''\n",
    "    def squeeze_excitation_layer(self, x):\n",
    " \n",
    "        squeeze = GlobalAveragePooling2D()(x)\n",
    "\n",
    "        excitation = Dense(units= self.filters3// 4)(squeeze)\n",
    "        excitation = tf.nn.relu(excitation)\n",
    "        excitation = Dense(units=self.filters3)(excitation)\n",
    "        excitation = tf.nn.sigmoid(excitation)\n",
    "        excitation = Reshape((1,1,self.filters3))(excitation)\n",
    "\n",
    "        scale = multiply([x,excitation])\n",
    "\n",
    "        return scale\n",
    "'''\n",
    "    def call(self, input_tensor, training=False):\n",
    "        x = self.conv2a(input_tensor)\n",
    "        x = self.bn2a(x, training=training)\n",
    "        x = tf.nn.relu(x)\n",
    "\n",
    "        x = self.conv2b(x)\n",
    "        x = self.bn2b(x, training=training)\n",
    "        x = tf.nn.relu(x)\n",
    "\n",
    "        x = self.conv2c(x)\n",
    "        x = self.bn2c(x, training=training)\n",
    "        #x = self.squeeze_excitation_layer(x)\n",
    "        squeeze = self.gap(x)\n",
    "\n",
    "        excitation = self.d1(squeeze)\n",
    "        excitation = tf.nn.relu(excitation)\n",
    "        excitation = self.d2(excitation)\n",
    "        excitation = tf.nn.sigmoid(excitation)\n",
    "        excitation = Reshape((1,1,self.filters3))(excitation)\n",
    "\n",
    "        x = multiply([x,excitation])\n",
    "        x += input_tensor\n",
    "        return tf.nn.relu(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\nclass Attention(tf.keras.Model):\\n    def __init__(self, kernel_size, filters):\\n        super(Attention, self).__init__(name='')\\n        filters1, filters2, filters3 = filters\\n\\n        self.conv2f = Conv2D(filters1, (1, 1),padding = 'same')\\n\\n        self.conv2g = Conv2D(filters2, (1, 1), padding='same')\\n        \\n        self.conv2h = Conv2D(filters3, (1, 1),padding = 'same')\\n        \\n        \\n    def call(self, input_tensor):\\n        def hw_flatten(x):\\n            return tf.reshape(x, shape=[tf.shape(x)[0], -1 , tf.shape(x)[-1]])\\n        s = tf.matmul(hw_flatten(self.conv2g(input_tensor)), hw_flatten(self.conv2f(input_tensor)), transpose_b=True) # # [bs, N, N]\\n\\n        beta = tf.nn.softmax(s)  # attention map\\n\\n        o = tf.matmul(beta, hw_flatten(self.conv2h(input_tensor))) # [bs, N, C]\\n        self.gamma = self.add_weight(name='gamma', shape=[1], initializer='uniform', trainable=True)\\n\\n        o = tf.reshape(o, shape=tf.shape(input_tensor)) # [bs, h, w, C]\\n        x = self.gamma * o + input_tensor\\n\\n        return x\\n\""
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "class Attention(tf.keras.Model):\n",
    "    def __init__(self, kernel_size, filters):\n",
    "        super(Attention, self).__init__(name='')\n",
    "        filters1, filters2, filters3 = filters\n",
    "\n",
    "        self.conv2f = Conv2D(filters1, (1, 1),padding = 'same')\n",
    "\n",
    "        self.conv2g = Conv2D(filters2, (1, 1), padding='same')\n",
    "        \n",
    "        self.conv2h = Conv2D(filters3, (1, 1),padding = 'same')\n",
    "        \n",
    "        \n",
    "    def call(self, input_tensor):\n",
    "        def hw_flatten(x):\n",
    "            return tf.reshape(x, shape=[tf.shape(x)[0], -1 , tf.shape(x)[-1]])\n",
    "        s = tf.matmul(hw_flatten(self.conv2g(input_tensor)), hw_flatten(self.conv2f(input_tensor)), transpose_b=True) # # [bs, N, N]\n",
    "\n",
    "        beta = tf.nn.softmax(s)  # attention map\n",
    "\n",
    "        o = tf.matmul(beta, hw_flatten(self.conv2h(input_tensor))) # [bs, N, C]\n",
    "        self.gamma = self.add_weight(name='gamma', shape=[1], initializer='uniform', trainable=True)\n",
    "\n",
    "        o = tf.reshape(o, shape=tf.shape(input_tensor)) # [bs, h, w, C]\n",
    "        x = self.gamma * o + input_tensor\n",
    "\n",
    "        return x\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rgb2yuv_conversion(x):   \n",
    "    return tf.image.rgb_to_yuv(x)[:,:,:,0:1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MNet base Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 463,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _make_divisible(v, divisor, min_value=None):\n",
    "    if min_value is None:\n",
    "        min_value = divisor\n",
    "    new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)\n",
    "    # Make sure that round down does not go down by more than 10%.\n",
    "    if new_v < 0.9 * v:\n",
    "        new_v += divisor\n",
    "    return new_v\n",
    "\n",
    "def swish(x):\n",
    "        \"\"\"Swish activation function: x * sigmoid(x).\n",
    "        Reference: [Searching for Activation Functions](https://arxiv.org/abs/1710.05941)\n",
    "        \"\"\"\n",
    "\n",
    "        return tf.nn.sigmoid(x) * x\n",
    "\n",
    "def MobileNetv2(input_shape, k, alpha=1.0):\n",
    "\n",
    "    inputs_img = Input(shape=input_shape,name = 'Input0')\n",
    "\n",
    "    first_filters = _make_divisible(16 * alpha, 8)\n",
    "    '''conv0'''\n",
    "    channel_axis = 1 if tf.keras.backend.image_data_format() == 'channels_first' else -1\n",
    "    x = Conv2D(16, (3,3), padding='same', strides=(1,1),name='conv0')(inputs_img)\n",
    "    x = BatchNormalization(axis=channel_axis,name='BN0')(x)\n",
    "    x = Activation(swish)(x) ##16\n",
    "    '''conv0'''\n",
    "    \n",
    "    '''_inverted_residual_block stage1 '''\n",
    "    inputs = x\n",
    "    channel_axis = 1 if tf.keras.backend.image_data_format() == 'channels_first' else -1\n",
    "    # Depth\n",
    "    tchannel = tf.keras.backend.int_shape(x)[channel_axis] * 1\n",
    "    # Width\n",
    "    cchannel = int(16 * 1)\n",
    "    x = Conv2D(tchannel, (1,1), padding='same', strides=(1,1),name='conv1')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name='BN1')(x)\n",
    "    x = Activation(swish)(x)\n",
    "    \n",
    "    x = DepthwiseConv2D((3,3), strides=(1, 1), depth_multiplier=1, padding='same',name = 'conv2')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN2')(x)\n",
    "    x = Activation(swish)(x)   \n",
    "    x = Conv2D(cchannel, (1,1), padding='same', strides=(1,1),name='conv3')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN3')(x)\n",
    "    #x = Add()([x, inputs])\n",
    "    ###################################\n",
    "    inputs = x\n",
    "    channel_axis = 1 if tf.keras.backend.image_data_format() == 'channels_first' else -1\n",
    "    # Depth\n",
    "    tchannel = tf.keras.backend.int_shape(x)[channel_axis] * 1\n",
    "    # Width\n",
    "    cchannel = int(16 * 1)\n",
    "    x = Conv2D(tchannel, (1,1), padding='same', strides=(1,1),name='conv4')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name='BN4')(x)\n",
    "    x = Activation(swish)(x)\n",
    "    \n",
    "    x = DepthwiseConv2D((3,3), strides=(1, 1), depth_multiplier=1, padding='same',name = 'conv5')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN5')(x)\n",
    "    x = Activation(swish)(x)   \n",
    "    x = Conv2D(cchannel, (1,1), padding='same', strides=(1,1),name='conv6')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN6')(x)\n",
    "    x = Add()([x, inputs])\n",
    "    '''_inverted_residual_block stage1'''\n",
    "    \n",
    "    x = Conv2D(16, (2,2), padding='same', strides=(2,2),name='conv7')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name='BN7')(x)\n",
    "    x = Activation(swish)(x)\n",
    "    \n",
    "    '''_inverted_residual_block stage2'''\n",
    "    \n",
    "    inputs = x\n",
    "    channel_axis = 1 if tf.keras.backend.image_data_format() == 'channels_first' else -1\n",
    "    # Depth\n",
    "    tchannel = tf.keras.backend.int_shape(x)[channel_axis] * 6\n",
    "    # Width\n",
    "    cchannel = int(32 * 1)\n",
    "    x = Conv2D(tchannel, (1,1), padding='same', strides=(1,1),name='conv8')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name='BN8')(x)\n",
    "    x = Activation(swish)(x)\n",
    "    \n",
    "    x = DepthwiseConv2D((3,3), strides=(1, 1), depth_multiplier=1, padding='same',name = 'conv9')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN9')(x)\n",
    "    x = Activation(swish)(x)   \n",
    "    x = Conv2D(cchannel, (1,1), padding='same', strides=(1,1),name='conv10')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN10')(x)\n",
    "    #x = Add()([x, inputs])\n",
    "    \n",
    "    ###################################\n",
    "    \n",
    "    inputs = x\n",
    "    channel_axis = 1 if tf.keras.backend.image_data_format() == 'channels_first' else -1\n",
    "    # Depth\n",
    "    tchannel = tf.keras.backend.int_shape(x)[channel_axis] * 6\n",
    "    # Width\n",
    "    cchannel = int(32 * 1)\n",
    "    x = Conv2D(tchannel, (1,1), padding='same', strides=(1,1),name='conv11')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name='BN11')(x)\n",
    "    x = Activation(swish)(x)\n",
    "    \n",
    "    x = DepthwiseConv2D((3,3), strides=(1, 1), depth_multiplier=1, padding='same',name = 'conv12')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN12')(x)\n",
    "    x = Activation(swish)(x)   \n",
    "    x = Conv2D(cchannel, (1,1), padding='same', strides=(1,1),name='conv13')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN13')(x)\n",
    "    x = Add()([x, inputs])\n",
    "    \n",
    "    inputs = x\n",
    "    channel_axis = 1 if tf.keras.backend.image_data_format() == 'channels_first' else -1\n",
    "    # Depth\n",
    "    tchannel = tf.keras.backend.int_shape(x)[channel_axis] * 6\n",
    "    # Width\n",
    "    cchannel = int(32 * 1)\n",
    "    x = Conv2D(tchannel, (1,1), padding='same', strides=(1,1),name='conv14')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name='BN14')(x)\n",
    "    x = Activation(swish)(x)\n",
    "    \n",
    "    x = DepthwiseConv2D((3,3), strides=(1, 1), depth_multiplier=1, padding='same',name = 'conv15')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN15')(x)\n",
    "    x = Activation(swish)(x)   \n",
    "    x = Conv2D(cchannel, (1,1), padding='same', strides=(1,1),name='conv16')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN16')(x)\n",
    "    x = Add()([x, inputs])\n",
    "    \n",
    "    '''_inverted_residual_block stage2'''\n",
    "    \n",
    "    x = Conv2D(32, (2,2), padding='same', strides=(2,2),name='conv17')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name='BN17')(x)\n",
    "    x = Activation(swish)(x)\n",
    "    \n",
    "    '''_inverted_residual_block stage3'''\n",
    "    #1\n",
    "    inputs = x\n",
    "    channel_axis = 1 if tf.keras.backend.image_data_format() == 'channels_first' else -1\n",
    "    # Depth\n",
    "    tchannel = tf.keras.backend.int_shape(x)[channel_axis] * 6\n",
    "    # Width\n",
    "    cchannel = int(64 * 1)\n",
    "    x = Conv2D(tchannel, (1,1), padding='same', strides=(1,1),name='conv18')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name='BN18')(x)\n",
    "    x = Activation(swish)(x)\n",
    "    \n",
    "    x = DepthwiseConv2D((3,3), strides=(1, 1), depth_multiplier=1, padding='same',name = 'conv19')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN19')(x)\n",
    "    x = Activation(swish)(x)   \n",
    "    x = Conv2D(cchannel, (1,1), padding='same', strides=(1,1),name='conv20')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN20')(x)\n",
    "    #x = Add()([x, inputs])\n",
    "    \n",
    "    ###################################\n",
    "    #2\n",
    "    inputs = x\n",
    "    channel_axis = 1 if tf.keras.backend.image_data_format() == 'channels_first' else -1\n",
    "    # Depth\n",
    "    tchannel = tf.keras.backend.int_shape(x)[channel_axis] * 6\n",
    "    # Width\n",
    "    cchannel = int(64 * 1)\n",
    "    x = Conv2D(tchannel, (1,1), padding='same', strides=(1,1),name='conv21')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name='BN22')(x)\n",
    "    x = Activation(swish)(x)\n",
    "    \n",
    "    x = DepthwiseConv2D((3,3), strides=(1, 1), depth_multiplier=1, padding='same',name = 'conv23')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN23')(x)\n",
    "    x = Activation(swish)(x)   \n",
    "    x = Conv2D(cchannel, (1,1), padding='same', strides=(1,1),name='conv24')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN24')(x)\n",
    "    x = Add()([x, inputs])\n",
    "    #3\n",
    "    inputs = x\n",
    "    channel_axis = 1 if tf.keras.backend.image_data_format() == 'channels_first' else -1\n",
    "    # Depth\n",
    "    tchannel = tf.keras.backend.int_shape(x)[channel_axis] * 6\n",
    "    # Width\n",
    "    cchannel = int(64 * 1)\n",
    "    x = Conv2D(tchannel, (1,1), padding='same', strides=(1,1),name='conv25')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name='BN25')(x)\n",
    "    x = Activation(swish)(x)\n",
    "    \n",
    "    x = DepthwiseConv2D((3,3), strides=(1, 1), depth_multiplier=1, padding='same',name = 'conv26')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN26')(x)\n",
    "    x = Activation(swish)(x)   \n",
    "    x = Conv2D(cchannel, (1,1), padding='same', strides=(1,1),name='conv27')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN27')(x)\n",
    "    x = Add()([x, inputs])\n",
    "    #4\n",
    "    inputs = x\n",
    "    channel_axis = 1 if tf.keras.backend.image_data_format() == 'channels_first' else -1\n",
    "    # Depth\n",
    "    tchannel = tf.keras.backend.int_shape(x)[channel_axis] * 6\n",
    "    # Width\n",
    "    cchannel = int(64 * 1)\n",
    "    x = Conv2D(tchannel, (1,1), padding='same', strides=(1,1),name='conv28')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name='BN28')(x)\n",
    "    x = Activation(swish)(x)\n",
    "    \n",
    "    x = DepthwiseConv2D((3,3), strides=(1, 1), depth_multiplier=1, padding='same',name = 'conv29')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN29')(x)\n",
    "    x = Activation(swish)(x)   \n",
    "    x = Conv2D(cchannel, (1,1), padding='same', strides=(1,1),name='conv30')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN30')(x)\n",
    "    x = Add()([x, inputs])\n",
    "    \n",
    "#    branch_2 = x\n",
    "    '''_inverted_residual_block stage3'''\n",
    "    \n",
    "    x = Conv2D(64, (2,2), padding='same', strides=(2,2),name='conv31')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name='BN31')(x)\n",
    "    x = Activation(swish)(x)\n",
    "    \n",
    "    \n",
    "    \n",
    "    '''branch_2'''\n",
    "#     branch_2 = Conv2D(128, (1,1), padding='same', strides=(1,1),name='b2_conv0')(branch_2)\n",
    "#     branch_2 = BatchNormalization(axis=channel_axis,name='b2_BN0')(branch_2)\n",
    "#     branch_2 = Activation(swish)(branch_2)\n",
    "    \n",
    "#     branch_2 = Conv2D(128, (2,2), padding='same', strides=(2,2),name='b2_conv1')(branch_2)\n",
    "#     branch_2 = BatchNormalization(axis=channel_axis,name='b2_BN1')(branch_2)\n",
    "#     branch_2 = Activation(swish)(branch_2)\n",
    "    \n",
    "#     branch_2 = Conv2D(320, (1,1), padding='same', strides=(1,1),name='b2_conv2')(branch_2)\n",
    "#     branch_2 = BatchNormalization(axis=channel_axis,name='b2_BN2')(branch_2)\n",
    "#     branch_2 = Activation(swish)(branch_2)\n",
    "#     branch_2 = GlobalAveragePooling2D()(branch_2)\n",
    "#     branch_2 = Reshape((1, 1, 320))(branch_2)\n",
    "#     branch_2 = Dropout(0.3, name='b2_Dropout')(branch_2)\n",
    "#     branch_2 = Conv2D(2, (1, 1), padding='same',name = 'b2_conv3')(branch_2)\n",
    "#     branch_2 = Activation('softmax', name='b2_softmax')(branch_2)\n",
    "#     b2_output = Reshape((2,))(branch_2)\n",
    "\n",
    "    \n",
    "    '''branch_2'''\n",
    "    \n",
    "    '''_inverted_residual_block stage4'''\n",
    "    #1\n",
    "    inputs = x\n",
    "    channel_axis = 1 if tf.keras.backend.image_data_format() == 'channels_first' else -1\n",
    "    # Depth\n",
    "    tchannel = tf.keras.backend.int_shape(x)[channel_axis] * 6\n",
    "    # Width\n",
    "    cchannel = int(96 * 1)\n",
    "    x = Conv2D(tchannel, (1,1), padding='same', strides=(1,1),name='conv32')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name='BN32')(x)\n",
    "    x = Activation(swish)(x)\n",
    "    \n",
    "    x = DepthwiseConv2D((3,3), strides=(1, 1), depth_multiplier=1, padding='same',name = 'conv33')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN33')(x)\n",
    "    x = Activation(swish)(x)   \n",
    "    x = Conv2D(cchannel, (1,1), padding='same', strides=(1,1),name='conv34')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN34')(x)\n",
    "    #x = Add()([x, inputs])\n",
    "    ###################################\n",
    "    #2\n",
    "    inputs = x\n",
    "    channel_axis = 1 if tf.keras.backend.image_data_format() == 'channels_first' else -1\n",
    "    # Depth\n",
    "    tchannel = tf.keras.backend.int_shape(x)[channel_axis] * 6\n",
    "    # Width\n",
    "    cchannel = int(96 * 1)\n",
    "    x = Conv2D(tchannel, (1,1), padding='same', strides=(1,1),name='conv35')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name='BN35')(x)\n",
    "    x = Activation(swish)(x)\n",
    "    \n",
    "    x = DepthwiseConv2D((3,3), strides=(1, 1), depth_multiplier=1, padding='same',name = 'conv36')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN36')(x)\n",
    "    x = Activation(swish)(x)   \n",
    "    x = Conv2D(cchannel, (1,1), padding='same', strides=(1,1),name='conv37')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN37')(x)\n",
    "    x = Add()([x, inputs])\n",
    "    \n",
    "    '''_inverted_residual_block stage4'''\n",
    "    \n",
    "    '''Final Stage'''\n",
    "    \n",
    "    if alpha > 1.0:\n",
    "        last_filters = _make_divisible(320 * alpha, 8)\n",
    "    else:\n",
    "        last_filters = 320\n",
    "\n",
    "        \n",
    "    x = Conv2D(320, (1,1), padding='same', strides=(1,1),name='conv38')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name='BN38')(x)\n",
    "    x = Activation(swish)(x)\n",
    "    x = GlobalAveragePooling2D()(x)\n",
    "    x = Reshape((1, 1, last_filters))(x)\n",
    "    x = Dropout(0.3, name='Dropout')(x)\n",
    "    x = Conv2D(k, (1, 1), padding='same',name = 'conv39')(x)\n",
    "    x = Activation('softmax', name='softmax')(x)\n",
    "    output = Reshape((k,))(x)\n",
    "\n",
    "    model = Model(inputs_img, output)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 464,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = MobileNetv2((32, 32, 3), 3, 1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 465,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_30\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "Input0 (InputLayer)             [(None, 32, 32, 3)]  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv0 (Conv2D)                  (None, 32, 32, 16)   448         Input0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "BN0 (BatchNormalization)        (None, 32, 32, 16)   64          conv0[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_967 (Activation)     (None, 32, 32, 16)   0           BN0[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "conv1 (Conv2D)                  (None, 32, 32, 16)   272         activation_967[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "BN1 (BatchNormalization)        (None, 32, 32, 16)   64          conv1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_968 (Activation)     (None, 32, 32, 16)   0           BN1[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "conv2 (DepthwiseConv2D)         (None, 32, 32, 16)   160         activation_968[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "BN2 (BatchNormalization)        (None, 32, 32, 16)   64          conv2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_969 (Activation)     (None, 32, 32, 16)   0           BN2[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "conv3 (Conv2D)                  (None, 32, 32, 16)   272         activation_969[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "BN3 (BatchNormalization)        (None, 32, 32, 16)   64          conv3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv4 (Conv2D)                  (None, 32, 32, 16)   272         BN3[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "BN4 (BatchNormalization)        (None, 32, 32, 16)   64          conv4[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_970 (Activation)     (None, 32, 32, 16)   0           BN4[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "conv5 (DepthwiseConv2D)         (None, 32, 32, 16)   160         activation_970[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "BN5 (BatchNormalization)        (None, 32, 32, 16)   64          conv5[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_971 (Activation)     (None, 32, 32, 16)   0           BN5[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "conv6 (Conv2D)                  (None, 32, 32, 16)   272         activation_971[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "BN6 (BatchNormalization)        (None, 32, 32, 16)   64          conv6[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "add_216 (Add)                   (None, 32, 32, 16)   0           BN6[0][0]                        \n",
      "                                                                 BN3[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "conv7 (Conv2D)                  (None, 16, 16, 16)   1040        add_216[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "BN7 (BatchNormalization)        (None, 16, 16, 16)   64          conv7[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_972 (Activation)     (None, 16, 16, 16)   0           BN7[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "conv8 (Conv2D)                  (None, 16, 16, 96)   1632        activation_972[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "BN8 (BatchNormalization)        (None, 16, 16, 96)   384         conv8[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_973 (Activation)     (None, 16, 16, 96)   0           BN8[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "conv9 (DepthwiseConv2D)         (None, 16, 16, 96)   960         activation_973[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "BN9 (BatchNormalization)        (None, 16, 16, 96)   384         conv9[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_974 (Activation)     (None, 16, 16, 96)   0           BN9[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "conv10 (Conv2D)                 (None, 16, 16, 32)   3104        activation_974[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "BN10 (BatchNormalization)       (None, 16, 16, 32)   128         conv10[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv11 (Conv2D)                 (None, 16, 16, 192)  6336        BN10[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "BN11 (BatchNormalization)       (None, 16, 16, 192)  768         conv11[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_975 (Activation)     (None, 16, 16, 192)  0           BN11[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "conv12 (DepthwiseConv2D)        (None, 16, 16, 192)  1920        activation_975[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "BN12 (BatchNormalization)       (None, 16, 16, 192)  768         conv12[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_976 (Activation)     (None, 16, 16, 192)  0           BN12[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "conv13 (Conv2D)                 (None, 16, 16, 32)   6176        activation_976[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "BN13 (BatchNormalization)       (None, 16, 16, 32)   128         conv13[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "add_217 (Add)                   (None, 16, 16, 32)   0           BN13[0][0]                       \n",
      "                                                                 BN10[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "conv14 (Conv2D)                 (None, 16, 16, 192)  6336        add_217[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "BN14 (BatchNormalization)       (None, 16, 16, 192)  768         conv14[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_977 (Activation)     (None, 16, 16, 192)  0           BN14[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "conv15 (DepthwiseConv2D)        (None, 16, 16, 192)  1920        activation_977[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "BN15 (BatchNormalization)       (None, 16, 16, 192)  768         conv15[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_978 (Activation)     (None, 16, 16, 192)  0           BN15[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "conv16 (Conv2D)                 (None, 16, 16, 32)   6176        activation_978[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "BN16 (BatchNormalization)       (None, 16, 16, 32)   128         conv16[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "add_218 (Add)                   (None, 16, 16, 32)   0           BN16[0][0]                       \n",
      "                                                                 add_217[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv17 (Conv2D)                 (None, 8, 8, 32)     4128        add_218[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "BN17 (BatchNormalization)       (None, 8, 8, 32)     128         conv17[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_979 (Activation)     (None, 8, 8, 32)     0           BN17[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "conv18 (Conv2D)                 (None, 8, 8, 192)    6336        activation_979[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "BN18 (BatchNormalization)       (None, 8, 8, 192)    768         conv18[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_980 (Activation)     (None, 8, 8, 192)    0           BN18[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "conv19 (DepthwiseConv2D)        (None, 8, 8, 192)    1920        activation_980[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "BN19 (BatchNormalization)       (None, 8, 8, 192)    768         conv19[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_981 (Activation)     (None, 8, 8, 192)    0           BN19[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "conv20 (Conv2D)                 (None, 8, 8, 64)     12352       activation_981[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "BN20 (BatchNormalization)       (None, 8, 8, 64)     256         conv20[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv21 (Conv2D)                 (None, 8, 8, 384)    24960       BN20[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "BN22 (BatchNormalization)       (None, 8, 8, 384)    1536        conv21[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_982 (Activation)     (None, 8, 8, 384)    0           BN22[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "conv23 (DepthwiseConv2D)        (None, 8, 8, 384)    3840        activation_982[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "BN23 (BatchNormalization)       (None, 8, 8, 384)    1536        conv23[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_983 (Activation)     (None, 8, 8, 384)    0           BN23[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "conv24 (Conv2D)                 (None, 8, 8, 64)     24640       activation_983[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "BN24 (BatchNormalization)       (None, 8, 8, 64)     256         conv24[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "add_219 (Add)                   (None, 8, 8, 64)     0           BN24[0][0]                       \n",
      "                                                                 BN20[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "conv25 (Conv2D)                 (None, 8, 8, 384)    24960       add_219[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "BN25 (BatchNormalization)       (None, 8, 8, 384)    1536        conv25[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_984 (Activation)     (None, 8, 8, 384)    0           BN25[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "conv26 (DepthwiseConv2D)        (None, 8, 8, 384)    3840        activation_984[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "BN26 (BatchNormalization)       (None, 8, 8, 384)    1536        conv26[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_985 (Activation)     (None, 8, 8, 384)    0           BN26[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "conv27 (Conv2D)                 (None, 8, 8, 64)     24640       activation_985[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "BN27 (BatchNormalization)       (None, 8, 8, 64)     256         conv27[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "add_220 (Add)                   (None, 8, 8, 64)     0           BN27[0][0]                       \n",
      "                                                                 add_219[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv28 (Conv2D)                 (None, 8, 8, 384)    24960       add_220[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "BN28 (BatchNormalization)       (None, 8, 8, 384)    1536        conv28[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_986 (Activation)     (None, 8, 8, 384)    0           BN28[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "conv29 (DepthwiseConv2D)        (None, 8, 8, 384)    3840        activation_986[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "BN29 (BatchNormalization)       (None, 8, 8, 384)    1536        conv29[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_987 (Activation)     (None, 8, 8, 384)    0           BN29[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "conv30 (Conv2D)                 (None, 8, 8, 64)     24640       activation_987[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "BN30 (BatchNormalization)       (None, 8, 8, 64)     256         conv30[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "add_221 (Add)                   (None, 8, 8, 64)     0           BN30[0][0]                       \n",
      "                                                                 add_220[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv31 (Conv2D)                 (None, 4, 4, 64)     16448       add_221[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "BN31 (BatchNormalization)       (None, 4, 4, 64)     256         conv31[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_988 (Activation)     (None, 4, 4, 64)     0           BN31[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "conv32 (Conv2D)                 (None, 4, 4, 384)    24960       activation_988[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "BN32 (BatchNormalization)       (None, 4, 4, 384)    1536        conv32[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_989 (Activation)     (None, 4, 4, 384)    0           BN32[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "conv33 (DepthwiseConv2D)        (None, 4, 4, 384)    3840        activation_989[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "BN33 (BatchNormalization)       (None, 4, 4, 384)    1536        conv33[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_990 (Activation)     (None, 4, 4, 384)    0           BN33[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "conv34 (Conv2D)                 (None, 4, 4, 96)     36960       activation_990[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "BN34 (BatchNormalization)       (None, 4, 4, 96)     384         conv34[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv35 (Conv2D)                 (None, 4, 4, 576)    55872       BN34[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "BN35 (BatchNormalization)       (None, 4, 4, 576)    2304        conv35[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_991 (Activation)     (None, 4, 4, 576)    0           BN35[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "conv36 (DepthwiseConv2D)        (None, 4, 4, 576)    5760        activation_991[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "BN36 (BatchNormalization)       (None, 4, 4, 576)    2304        conv36[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_992 (Activation)     (None, 4, 4, 576)    0           BN36[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "conv37 (Conv2D)                 (None, 4, 4, 96)     55392       activation_992[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "BN37 (BatchNormalization)       (None, 4, 4, 96)     384         conv37[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "add_222 (Add)                   (None, 4, 4, 96)     0           BN37[0][0]                       \n",
      "                                                                 BN34[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "conv38 (Conv2D)                 (None, 4, 4, 320)    31040       add_222[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "BN38 (BatchNormalization)       (None, 4, 4, 320)    1280        conv38[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_993 (Activation)     (None, 4, 4, 320)    0           BN38[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling2d_75 (Gl (None, 320)          0           activation_993[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "reshape_150 (Reshape)           (None, 1, 1, 320)    0           global_average_pooling2d_75[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "Dropout (Dropout)               (None, 1, 1, 320)    0           reshape_150[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv39 (Conv2D)                 (None, 1, 1, 3)      963         Dropout[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "softmax (Activation)            (None, 1, 1, 3)      0           conv39[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "reshape_151 (Reshape)           (None, 3)            0           softmax[0][0]                    \n",
      "==================================================================================================\n",
      "Total params: 480,371\n",
      "Trainable params: 467,059\n",
      "Non-trainable params: 13,312\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 466,
   "metadata": {},
   "outputs": [],
   "source": [
    "freeze_layer_name = []\n",
    "for layer in model.layers[:]:\n",
    "    freeze_layer_name.append(layer.name)\n",
    "    layer.trainable =  False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 467,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Input0', 'conv0', 'BN0', 'activation_967', 'conv1', 'BN1', 'activation_968', 'conv2', 'BN2', 'activation_969', 'conv3', 'BN3', 'conv4', 'BN4', 'activation_970', 'conv5', 'BN5', 'activation_971', 'conv6', 'BN6', 'add_216', 'conv7', 'BN7', 'activation_972', 'conv8', 'BN8', 'activation_973', 'conv9', 'BN9', 'activation_974', 'conv10', 'BN10', 'conv11', 'BN11', 'activation_975', 'conv12', 'BN12', 'activation_976', 'conv13', 'BN13', 'add_217', 'conv14', 'BN14', 'activation_977', 'conv15', 'BN15', 'activation_978', 'conv16', 'BN16', 'add_218', 'conv17', 'BN17', 'activation_979', 'conv18', 'BN18', 'activation_980', 'conv19', 'BN19', 'activation_981', 'conv20', 'BN20', 'conv21', 'BN22', 'activation_982', 'conv23', 'BN23', 'activation_983', 'conv24', 'BN24', 'add_219', 'conv25', 'BN25', 'activation_984', 'conv26', 'BN26', 'activation_985', 'conv27', 'BN27', 'add_220', 'conv28', 'BN28', 'activation_986', 'conv29', 'BN29', 'activation_987', 'conv30', 'BN30', 'add_221', 'conv31', 'BN31', 'activation_988', 'conv32', 'BN32', 'activation_989', 'conv33', 'BN33', 'activation_990', 'conv34', 'BN34', 'conv35', 'BN35', 'activation_991', 'conv36', 'BN36', 'activation_992', 'conv37', 'BN37', 'add_222', 'conv38', 'BN38', 'activation_993', 'global_average_pooling2d_75', 'reshape_150', 'Dropout', 'conv39', 'softmax', 'reshape_151']\n"
     ]
    }
   ],
   "source": [
    "print(freeze_layer_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MNet two branch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 468,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _make_divisible(v, divisor, min_value=None):\n",
    "    if min_value is None:\n",
    "        min_value = divisor\n",
    "    new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)\n",
    "    # Make sure that round down does not go down by more than 10%.\n",
    "    if new_v < 0.9 * v:\n",
    "        new_v += divisor\n",
    "    return new_v\n",
    "\n",
    "def swish(x):\n",
    "        \"\"\"Swish activation function: x * sigmoid(x).\n",
    "        Reference: [Searching for Activation Functions](https://arxiv.org/abs/1710.05941)\n",
    "        \"\"\"\n",
    "\n",
    "        return tf.nn.sigmoid(x) * x\n",
    "\n",
    "def MobileNetv2(input_shape, k, alpha=1.0):\n",
    "    \n",
    "    inputs_img = Input(shape=input_shape,name = 'Input0')\n",
    "\n",
    "    first_filters = _make_divisible(16 * alpha, 8)\n",
    "    '''conv0'''\n",
    "    channel_axis = 1 if tf.keras.backend.image_data_format() == 'channels_first' else -1\n",
    "    x = Conv2D(16, (3,3), padding='same', strides=(1,1),name='conv0')(inputs_img)\n",
    "    x = BatchNormalization(axis=channel_axis,name='BN0')(x)\n",
    "    x = Activation(swish)(x) ##16\n",
    "    '''conv0'''\n",
    "    \n",
    "    '''_inverted_residual_block stage1 '''\n",
    "    inputs = x\n",
    "    channel_axis = 1 if tf.keras.backend.image_data_format() == 'channels_first' else -1\n",
    "    # Depth\n",
    "    tchannel = tf.keras.backend.int_shape(x)[channel_axis] * 1\n",
    "    # Width\n",
    "    cchannel = int(16 * 1)\n",
    "    x = Conv2D(tchannel, (1,1), padding='same', strides=(1,1),name='conv1')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name='BN1')(x)\n",
    "    x = Activation(swish)(x)\n",
    "    \n",
    "    x = DepthwiseConv2D((3,3), strides=(1, 1), depth_multiplier=1, padding='same',name = 'conv2')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN2')(x)\n",
    "    x = Activation(swish)(x)   \n",
    "    x = Conv2D(cchannel, (1,1), padding='same', strides=(1,1),name='conv3')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN3')(x)\n",
    "    #x = Add()([x, inputs])\n",
    "    ###################################\n",
    "    inputs = x\n",
    "    channel_axis = 1 if tf.keras.backend.image_data_format() == 'channels_first' else -1\n",
    "    # Depth\n",
    "    tchannel = tf.keras.backend.int_shape(x)[channel_axis] * 1\n",
    "    # Width\n",
    "    cchannel = int(16 * 1)\n",
    "    x = Conv2D(tchannel, (1,1), padding='same', strides=(1,1),name='conv4')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name='BN4')(x)\n",
    "    x = Activation(swish)(x)\n",
    "    \n",
    "    x = DepthwiseConv2D((3,3), strides=(1, 1), depth_multiplier=1, padding='same',name = 'conv5')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN5')(x)\n",
    "    x = Activation(swish)(x)   \n",
    "    x = Conv2D(cchannel, (1,1), padding='same', strides=(1,1),name='conv6')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN6')(x)\n",
    "    x = Add()([x, inputs])\n",
    "    '''_inverted_residual_block stage1'''\n",
    "    \n",
    "    x = Conv2D(16, (2,2), padding='same', strides=(2,2),name='conv7')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name='BN7')(x)\n",
    "    x = Activation(swish)(x)\n",
    "    \n",
    "    '''_inverted_residual_block stage2'''\n",
    "    \n",
    "    inputs = x\n",
    "    channel_axis = 1 if tf.keras.backend.image_data_format() == 'channels_first' else -1\n",
    "    # Depth\n",
    "    tchannel = tf.keras.backend.int_shape(x)[channel_axis] * 6\n",
    "    # Width\n",
    "    cchannel = int(32 * 1)\n",
    "    x = Conv2D(tchannel, (1,1), padding='same', strides=(1,1),name='conv8')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name='BN8')(x)\n",
    "    x = Activation(swish)(x)\n",
    "    \n",
    "    x = DepthwiseConv2D((3,3), strides=(1, 1), depth_multiplier=1, padding='same',name = 'conv9')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN9')(x)\n",
    "    x = Activation(swish)(x)   \n",
    "    x = Conv2D(cchannel, (1,1), padding='same', strides=(1,1),name='conv10')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN10')(x)\n",
    "    #x = Add()([x, inputs])\n",
    "    \n",
    "    ###################################\n",
    "    \n",
    "    inputs = x\n",
    "    channel_axis = 1 if tf.keras.backend.image_data_format() == 'channels_first' else -1\n",
    "    # Depth\n",
    "    tchannel = tf.keras.backend.int_shape(x)[channel_axis] * 6\n",
    "    # Width\n",
    "    cchannel = int(32 * 1)\n",
    "    x = Conv2D(tchannel, (1,1), padding='same', strides=(1,1),name='conv11')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name='BN11')(x)\n",
    "    x = Activation(swish)(x)\n",
    "    \n",
    "    x = DepthwiseConv2D((3,3), strides=(1, 1), depth_multiplier=1, padding='same',name = 'conv12')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN12')(x)\n",
    "    x = Activation(swish)(x)   \n",
    "    x = Conv2D(cchannel, (1,1), padding='same', strides=(1,1),name='conv13')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN13')(x)\n",
    "    x = Add()([x, inputs])\n",
    "    \n",
    "    inputs = x\n",
    "    channel_axis = 1 if tf.keras.backend.image_data_format() == 'channels_first' else -1\n",
    "    # Depth\n",
    "    tchannel = tf.keras.backend.int_shape(x)[channel_axis] * 6\n",
    "    # Width\n",
    "    cchannel = int(32 * 1)\n",
    "    x = Conv2D(tchannel, (1,1), padding='same', strides=(1,1),name='conv14')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name='BN14')(x)\n",
    "    x = Activation(swish)(x)\n",
    "    \n",
    "    x = DepthwiseConv2D((3,3), strides=(1, 1), depth_multiplier=1, padding='same',name = 'conv15')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN15')(x)\n",
    "    x = Activation(swish)(x)   \n",
    "    x = Conv2D(cchannel, (1,1), padding='same', strides=(1,1),name='conv16')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN16')(x)\n",
    "    x = Add()([x, inputs])\n",
    "    \n",
    "    '''_inverted_residual_block stage2'''\n",
    "    \n",
    "    x = Conv2D(32, (2,2), padding='same', strides=(2,2),name='conv17')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name='BN17')(x)\n",
    "    x = Activation(swish)(x)\n",
    "    \n",
    "    '''_inverted_residual_block stage3'''\n",
    "    #1\n",
    "    inputs = x\n",
    "    channel_axis = 1 if tf.keras.backend.image_data_format() == 'channels_first' else -1\n",
    "    # Depth\n",
    "    tchannel = tf.keras.backend.int_shape(x)[channel_axis] * 6\n",
    "    # Width\n",
    "    cchannel = int(64 * 1)\n",
    "    x = Conv2D(tchannel, (1,1), padding='same', strides=(1,1),name='conv18')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name='BN18')(x)\n",
    "    x = Activation(swish)(x)\n",
    "    \n",
    "    x = DepthwiseConv2D((3,3), strides=(1, 1), depth_multiplier=1, padding='same',name = 'conv19')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN19')(x)\n",
    "    x = Activation(swish)(x)   \n",
    "    x = Conv2D(cchannel, (1,1), padding='same', strides=(1,1),name='conv20')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN20')(x)\n",
    "    #x = Add()([x, inputs])\n",
    "    \n",
    "    ###################################\n",
    "    #2\n",
    "    inputs = x\n",
    "    channel_axis = 1 if tf.keras.backend.image_data_format() == 'channels_first' else -1\n",
    "    # Depth\n",
    "    tchannel = tf.keras.backend.int_shape(x)[channel_axis] * 6\n",
    "    # Width\n",
    "    cchannel = int(64 * 1)\n",
    "    x = Conv2D(tchannel, (1,1), padding='same', strides=(1,1),name='conv21')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name='BN22')(x)\n",
    "    x = Activation(swish)(x)\n",
    "    \n",
    "    x = DepthwiseConv2D((3,3), strides=(1, 1), depth_multiplier=1, padding='same',name = 'conv23')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN23')(x)\n",
    "    x = Activation(swish)(x)   \n",
    "    x = Conv2D(cchannel, (1,1), padding='same', strides=(1,1),name='conv24')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN24')(x)\n",
    "    x = Add()([x, inputs])\n",
    "    #3\n",
    "    inputs = x\n",
    "    channel_axis = 1 if tf.keras.backend.image_data_format() == 'channels_first' else -1\n",
    "    # Depth\n",
    "    tchannel = tf.keras.backend.int_shape(x)[channel_axis] * 6\n",
    "    # Width\n",
    "    cchannel = int(64 * 1)\n",
    "    x = Conv2D(tchannel, (1,1), padding='same', strides=(1,1),name='conv25')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name='BN25')(x)\n",
    "    x = Activation(swish)(x)\n",
    "    \n",
    "    x = DepthwiseConv2D((3,3), strides=(1, 1), depth_multiplier=1, padding='same',name = 'conv26')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN26')(x)\n",
    "    x = Activation(swish)(x)   \n",
    "    x = Conv2D(cchannel, (1,1), padding='same', strides=(1,1),name='conv27')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN27')(x)\n",
    "    x = Add()([x, inputs])\n",
    "    #4\n",
    "    inputs = x\n",
    "    channel_axis = 1 if tf.keras.backend.image_data_format() == 'channels_first' else -1\n",
    "    # Depth\n",
    "    tchannel = tf.keras.backend.int_shape(x)[channel_axis] * 6\n",
    "    # Width\n",
    "    cchannel = int(64 * 1)\n",
    "    x = Conv2D(tchannel, (1,1), padding='same', strides=(1,1),name='conv28')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name='BN28')(x)\n",
    "    x = Activation(swish)(x)\n",
    "    \n",
    "    x = DepthwiseConv2D((3,3), strides=(1, 1), depth_multiplier=1, padding='same',name = 'conv29')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN29')(x)\n",
    "    x = Activation(swish)(x)   \n",
    "    x = Conv2D(cchannel, (1,1), padding='same', strides=(1,1),name='conv30')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN30')(x)\n",
    "    x = Add()([x, inputs])\n",
    "    \n",
    "    branch_tt = x\n",
    "\n",
    "    '''_inverted_residual_block stage3'''\n",
    "    \n",
    "    x = Conv2D(64, (2,2), padding='same', strides=(2,2),name='conv31')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name='BN31')(x)\n",
    "    x = Activation(swish)(x)\n",
    "    \n",
    "    \n",
    "    '''bracnch_tt'''\n",
    "    channel_axis = 1 if tf.keras.backend.image_data_format() == 'channels_first' else -1\n",
    "    # Depth\n",
    "    tchannel = tf.keras.backend.int_shape(branch_tt)[channel_axis] * 6\n",
    "    # Width\n",
    "    cchannel = int(96 * 1)\n",
    "    branch_tt = Conv2D(tchannel, (1,1), padding='same', strides=(1,1),name='b5_conv0')(branch_tt)\n",
    "    branch_tt = BatchNormalization(axis=channel_axis,name='b5_BN0')(branch_tt)\n",
    "    branch_tt = Activation(swish)(branch_tt)\n",
    "    \n",
    "    branch_tt = DepthwiseConv2D((3,3), strides=(1, 1), depth_multiplier=1, padding='same',name = 'b5_conv1')(branch_tt)\n",
    "    branch_tt = BatchNormalization(axis=channel_axis,name = 'b5_BN1')(branch_tt)\n",
    "    branch_tt = Activation(swish)(branch_tt)   \n",
    "    branch_tt = Conv2D(cchannel, (1,1), padding='same', strides=(1,1),name='b5_conv2')(branch_tt)\n",
    "    branch_tt = BatchNormalization(axis=channel_axis,name = 'b5_BN2')(branch_tt)\n",
    "    \n",
    "    branch_tt = Conv2D(320, (1,1), padding='same', strides=(1,1),name='b5_conv3')(branch_tt)\n",
    "    branch_tt = BatchNormalization(axis=channel_axis,name='b5_BN3')(branch_tt)\n",
    "    branch_tt = Activation(swish)(branch_tt)\n",
    "    branch_tt = GlobalAveragePooling2D()(branch_tt)\n",
    "    branch_tt = Reshape((1, 1, 320))(branch_tt)\n",
    "    branch_tt = Dropout(0.3, name='b5_Dropout')(branch_tt)\n",
    "    branch_tt = Conv2D(2, (1, 1), padding='same',name = 'b5_conv4')(branch_tt)\n",
    "    branch_tt = Activation('softmax', name='b5_softmax')(branch_tt)\n",
    "    b5_output = Reshape((2,))(branch_tt)\n",
    "    \n",
    "    \n",
    "    \n",
    " \n",
    "\n",
    "    \n",
    "    '''_inverted_residual_block stage4'''\n",
    "    #1\n",
    "    inputs = x\n",
    "    channel_axis = 1 if tf.keras.backend.image_data_format() == 'channels_first' else -1\n",
    "    # Depth\n",
    "    tchannel = tf.keras.backend.int_shape(x)[channel_axis] * 6\n",
    "    # Width\n",
    "    cchannel = int(96 * 1)\n",
    "    x = Conv2D(tchannel, (1,1), padding='same', strides=(1,1),name='conv32')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name='BN32')(x)\n",
    "    x = Activation(swish)(x)\n",
    "    \n",
    "    x = DepthwiseConv2D((3,3), strides=(1, 1), depth_multiplier=1, padding='same',name = 'conv33')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN33')(x)\n",
    "    x = Activation(swish)(x)   \n",
    "    x = Conv2D(cchannel, (1,1), padding='same', strides=(1,1),name='conv34')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN34')(x)\n",
    "    #x = Add()([x, inputs])\n",
    "    ###################################\n",
    "    #2\n",
    "    inputs = x\n",
    "    channel_axis = 1 if tf.keras.backend.image_data_format() == 'channels_first' else -1\n",
    "    # Depth\n",
    "    tchannel = tf.keras.backend.int_shape(x)[channel_axis] * 6\n",
    "    # Width\n",
    "    cchannel = int(96 * 1)\n",
    "    x = Conv2D(tchannel, (1,1), padding='same', strides=(1,1),name='conv35')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name='BN35')(x)\n",
    "    x = Activation(swish)(x)\n",
    "    \n",
    "    x = DepthwiseConv2D((3,3), strides=(1, 1), depth_multiplier=1, padding='same',name = 'conv36')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN36')(x)\n",
    "    x = Activation(swish)(x)   \n",
    "    x = Conv2D(cchannel, (1,1), padding='same', strides=(1,1),name='conv37')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN37')(x)\n",
    "    x = Add()([x, inputs])\n",
    "    \n",
    "    '''_inverted_residual_block stage4'''\n",
    "    \n",
    "    '''Final Stage'''\n",
    "    \n",
    "    if alpha > 1.0:\n",
    "        last_filters = _make_divisible(320 * alpha, 8)\n",
    "    else:\n",
    "        last_filters = 320\n",
    "\n",
    "        \n",
    "    x = Conv2D(320, (1,1), padding='same', strides=(1,1),name='conv38')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name='BN38')(x)\n",
    "    x = Activation(swish)(x)\n",
    "    x = GlobalAveragePooling2D()(x)\n",
    "    x = Reshape((1, 1, last_filters))(x)\n",
    "    x = Dropout(0.3, name='Dropout')(x)\n",
    "    x = Conv2D(k, (1, 1), padding='same',name = 'conv39')(x)\n",
    "    x = Activation('softmax', name='softmax')(x)\n",
    "    output = Reshape((k,))(x)\n",
    "\n",
    "    model = Model(inputs_img, b5_output)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 469,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = MobileNetv2((32, 32, 3), 3, 1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 470,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_31\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "Input0 (InputLayer)             [(None, 32, 32, 3)]  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv0 (Conv2D)                  (None, 32, 32, 16)   448         Input0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "BN0 (BatchNormalization)        (None, 32, 32, 16)   64          conv0[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_994 (Activation)     (None, 32, 32, 16)   0           BN0[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "conv1 (Conv2D)                  (None, 32, 32, 16)   272         activation_994[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "BN1 (BatchNormalization)        (None, 32, 32, 16)   64          conv1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_995 (Activation)     (None, 32, 32, 16)   0           BN1[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "conv2 (DepthwiseConv2D)         (None, 32, 32, 16)   160         activation_995[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "BN2 (BatchNormalization)        (None, 32, 32, 16)   64          conv2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_996 (Activation)     (None, 32, 32, 16)   0           BN2[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "conv3 (Conv2D)                  (None, 32, 32, 16)   272         activation_996[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "BN3 (BatchNormalization)        (None, 32, 32, 16)   64          conv3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv4 (Conv2D)                  (None, 32, 32, 16)   272         BN3[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "BN4 (BatchNormalization)        (None, 32, 32, 16)   64          conv4[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_997 (Activation)     (None, 32, 32, 16)   0           BN4[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "conv5 (DepthwiseConv2D)         (None, 32, 32, 16)   160         activation_997[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "BN5 (BatchNormalization)        (None, 32, 32, 16)   64          conv5[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_998 (Activation)     (None, 32, 32, 16)   0           BN5[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "conv6 (Conv2D)                  (None, 32, 32, 16)   272         activation_998[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "BN6 (BatchNormalization)        (None, 32, 32, 16)   64          conv6[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "add_223 (Add)                   (None, 32, 32, 16)   0           BN6[0][0]                        \n",
      "                                                                 BN3[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "conv7 (Conv2D)                  (None, 16, 16, 16)   1040        add_223[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "BN7 (BatchNormalization)        (None, 16, 16, 16)   64          conv7[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_999 (Activation)     (None, 16, 16, 16)   0           BN7[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "conv8 (Conv2D)                  (None, 16, 16, 96)   1632        activation_999[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "BN8 (BatchNormalization)        (None, 16, 16, 96)   384         conv8[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_1000 (Activation)    (None, 16, 16, 96)   0           BN8[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "conv9 (DepthwiseConv2D)         (None, 16, 16, 96)   960         activation_1000[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "BN9 (BatchNormalization)        (None, 16, 16, 96)   384         conv9[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_1001 (Activation)    (None, 16, 16, 96)   0           BN9[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "conv10 (Conv2D)                 (None, 16, 16, 32)   3104        activation_1001[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "BN10 (BatchNormalization)       (None, 16, 16, 32)   128         conv10[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv11 (Conv2D)                 (None, 16, 16, 192)  6336        BN10[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "BN11 (BatchNormalization)       (None, 16, 16, 192)  768         conv11[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_1002 (Activation)    (None, 16, 16, 192)  0           BN11[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "conv12 (DepthwiseConv2D)        (None, 16, 16, 192)  1920        activation_1002[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "BN12 (BatchNormalization)       (None, 16, 16, 192)  768         conv12[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_1003 (Activation)    (None, 16, 16, 192)  0           BN12[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "conv13 (Conv2D)                 (None, 16, 16, 32)   6176        activation_1003[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "BN13 (BatchNormalization)       (None, 16, 16, 32)   128         conv13[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "add_224 (Add)                   (None, 16, 16, 32)   0           BN13[0][0]                       \n",
      "                                                                 BN10[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "conv14 (Conv2D)                 (None, 16, 16, 192)  6336        add_224[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "BN14 (BatchNormalization)       (None, 16, 16, 192)  768         conv14[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_1004 (Activation)    (None, 16, 16, 192)  0           BN14[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "conv15 (DepthwiseConv2D)        (None, 16, 16, 192)  1920        activation_1004[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "BN15 (BatchNormalization)       (None, 16, 16, 192)  768         conv15[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_1005 (Activation)    (None, 16, 16, 192)  0           BN15[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "conv16 (Conv2D)                 (None, 16, 16, 32)   6176        activation_1005[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "BN16 (BatchNormalization)       (None, 16, 16, 32)   128         conv16[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "add_225 (Add)                   (None, 16, 16, 32)   0           BN16[0][0]                       \n",
      "                                                                 add_224[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv17 (Conv2D)                 (None, 8, 8, 32)     4128        add_225[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "BN17 (BatchNormalization)       (None, 8, 8, 32)     128         conv17[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_1006 (Activation)    (None, 8, 8, 32)     0           BN17[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "conv18 (Conv2D)                 (None, 8, 8, 192)    6336        activation_1006[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "BN18 (BatchNormalization)       (None, 8, 8, 192)    768         conv18[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_1007 (Activation)    (None, 8, 8, 192)    0           BN18[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "conv19 (DepthwiseConv2D)        (None, 8, 8, 192)    1920        activation_1007[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "BN19 (BatchNormalization)       (None, 8, 8, 192)    768         conv19[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_1008 (Activation)    (None, 8, 8, 192)    0           BN19[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "conv20 (Conv2D)                 (None, 8, 8, 64)     12352       activation_1008[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "BN20 (BatchNormalization)       (None, 8, 8, 64)     256         conv20[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv21 (Conv2D)                 (None, 8, 8, 384)    24960       BN20[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "BN22 (BatchNormalization)       (None, 8, 8, 384)    1536        conv21[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_1009 (Activation)    (None, 8, 8, 384)    0           BN22[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "conv23 (DepthwiseConv2D)        (None, 8, 8, 384)    3840        activation_1009[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "BN23 (BatchNormalization)       (None, 8, 8, 384)    1536        conv23[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_1010 (Activation)    (None, 8, 8, 384)    0           BN23[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "conv24 (Conv2D)                 (None, 8, 8, 64)     24640       activation_1010[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "BN24 (BatchNormalization)       (None, 8, 8, 64)     256         conv24[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "add_226 (Add)                   (None, 8, 8, 64)     0           BN24[0][0]                       \n",
      "                                                                 BN20[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "conv25 (Conv2D)                 (None, 8, 8, 384)    24960       add_226[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "BN25 (BatchNormalization)       (None, 8, 8, 384)    1536        conv25[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_1011 (Activation)    (None, 8, 8, 384)    0           BN25[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "conv26 (DepthwiseConv2D)        (None, 8, 8, 384)    3840        activation_1011[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "BN26 (BatchNormalization)       (None, 8, 8, 384)    1536        conv26[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_1012 (Activation)    (None, 8, 8, 384)    0           BN26[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "conv27 (Conv2D)                 (None, 8, 8, 64)     24640       activation_1012[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "BN27 (BatchNormalization)       (None, 8, 8, 64)     256         conv27[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "add_227 (Add)                   (None, 8, 8, 64)     0           BN27[0][0]                       \n",
      "                                                                 add_226[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv28 (Conv2D)                 (None, 8, 8, 384)    24960       add_227[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "BN28 (BatchNormalization)       (None, 8, 8, 384)    1536        conv28[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_1013 (Activation)    (None, 8, 8, 384)    0           BN28[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "conv29 (DepthwiseConv2D)        (None, 8, 8, 384)    3840        activation_1013[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "BN29 (BatchNormalization)       (None, 8, 8, 384)    1536        conv29[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_1014 (Activation)    (None, 8, 8, 384)    0           BN29[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "conv30 (Conv2D)                 (None, 8, 8, 64)     24640       activation_1014[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "BN30 (BatchNormalization)       (None, 8, 8, 64)     256         conv30[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "add_228 (Add)                   (None, 8, 8, 64)     0           BN30[0][0]                       \n",
      "                                                                 add_227[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "b5_conv0 (Conv2D)               (None, 8, 8, 384)    24960       add_228[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "b5_BN0 (BatchNormalization)     (None, 8, 8, 384)    1536        b5_conv0[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_1016 (Activation)    (None, 8, 8, 384)    0           b5_BN0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "b5_conv1 (DepthwiseConv2D)      (None, 8, 8, 384)    3840        activation_1016[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "b5_BN1 (BatchNormalization)     (None, 8, 8, 384)    1536        b5_conv1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_1017 (Activation)    (None, 8, 8, 384)    0           b5_BN1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "b5_conv2 (Conv2D)               (None, 8, 8, 96)     36960       activation_1017[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "b5_BN2 (BatchNormalization)     (None, 8, 8, 96)     384         b5_conv2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "b5_conv3 (Conv2D)               (None, 8, 8, 320)    31040       b5_BN2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "b5_BN3 (BatchNormalization)     (None, 8, 8, 320)    1280        b5_conv3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_1018 (Activation)    (None, 8, 8, 320)    0           b5_BN3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling2d_76 (Gl (None, 320)          0           activation_1018[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "reshape_152 (Reshape)           (None, 1, 1, 320)    0           global_average_pooling2d_76[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "b5_Dropout (Dropout)            (None, 1, 1, 320)    0           reshape_152[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "b5_conv4 (Conv2D)               (None, 1, 1, 2)      642         b5_Dropout[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "b5_softmax (Activation)         (None, 1, 1, 2)      0           b5_conv4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "reshape_153 (Reshape)           (None, 2)            0           b5_softmax[0][0]                 \n",
      "==================================================================================================\n",
      "Total params: 341,330\n",
      "Trainable params: 330,642\n",
      "Non-trainable params: 10,688\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 471,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "138"
      ]
     },
     "execution_count": 471,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(model.trainable_variables)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 472,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "activation_994\n",
      "activation_995\n",
      "activation_996\n",
      "activation_997\n",
      "activation_998\n",
      "add_223\n",
      "activation_999\n",
      "activation_1000\n",
      "activation_1001\n",
      "activation_1002\n",
      "activation_1003\n",
      "add_224\n",
      "activation_1004\n",
      "activation_1005\n",
      "add_225\n",
      "activation_1006\n",
      "activation_1007\n",
      "activation_1008\n",
      "activation_1009\n",
      "activation_1010\n",
      "add_226\n",
      "activation_1011\n",
      "activation_1012\n",
      "add_227\n",
      "activation_1013\n",
      "activation_1014\n",
      "add_228\n",
      "b5_conv0\n",
      "b5_BN0\n",
      "activation_1016\n",
      "b5_conv1\n",
      "b5_BN1\n",
      "activation_1017\n",
      "b5_conv2\n",
      "b5_BN2\n",
      "b5_conv3\n",
      "b5_BN3\n",
      "activation_1018\n",
      "global_average_pooling2d_76\n",
      "reshape_152\n",
      "b5_Dropout\n",
      "b5_conv4\n",
      "b5_softmax\n",
      "reshape_153\n"
     ]
    }
   ],
   "source": [
    "for layer in model.layers[:]:\n",
    "    if layer.name in freeze_layer_name:\n",
    "        layer.trainable =  False\n",
    "    else:\n",
    "        print(layer.name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 473,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18"
      ]
     },
     "execution_count": 473,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(model.trainable_variables)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MBNet_B3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _make_divisible(v, divisor, min_value=None):\n",
    "    if min_value is None:\n",
    "        min_value = divisor\n",
    "    new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)\n",
    "    # Make sure that round down does not go down by more than 10%.\n",
    "    if new_v < 0.9 * v:\n",
    "        new_v += divisor\n",
    "    return new_v\n",
    "\n",
    "def swish(x):\n",
    "        \"\"\"Swish activation function: x * sigmoid(x).\n",
    "        Reference: [Searching for Activation Functions](https://arxiv.org/abs/1710.05941)\n",
    "        \"\"\"\n",
    "\n",
    "        return tf.nn.sigmoid(x) * x\n",
    "\n",
    "def MobileNetv2(input_shape, k, alpha=1.0):\n",
    "    \n",
    "    inputs_img = Input(shape=input_shape,name = 'Input0')\n",
    "\n",
    "    first_filters = _make_divisible(16 * alpha, 8)\n",
    "    '''conv0'''\n",
    "    channel_axis = 1 if tf.keras.backend.image_data_format() == 'channels_first' else -1\n",
    "    x = Conv2D(16, (3,3), padding='same', strides=(1,1),name='conv0')(inputs_img)\n",
    "    x = BatchNormalization(axis=channel_axis,name='BN0')(x)\n",
    "    x = Activation(swish)(x) ##16\n",
    "    '''conv0'''\n",
    "    \n",
    "    '''_inverted_residual_block stage1 '''\n",
    "    inputs = x\n",
    "    channel_axis = 1 if tf.keras.backend.image_data_format() == 'channels_first' else -1\n",
    "    # Depth\n",
    "    tchannel = tf.keras.backend.int_shape(x)[channel_axis] * 1\n",
    "    # Width\n",
    "    cchannel = int(16 * 1)\n",
    "    x = Conv2D(tchannel, (1,1), padding='same', strides=(1,1),name='conv1')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name='BN1')(x)\n",
    "    x = Activation(swish)(x)\n",
    "    \n",
    "    x = DepthwiseConv2D((3,3), strides=(1, 1), depth_multiplier=1, padding='same',name = 'conv2')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN2')(x)\n",
    "    x = Activation(swish)(x)   \n",
    "    x = Conv2D(cchannel, (1,1), padding='same', strides=(1,1),name='conv3')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN3')(x)\n",
    "    #x = Add()([x, inputs])\n",
    "    ###################################\n",
    "    inputs = x\n",
    "    channel_axis = 1 if tf.keras.backend.image_data_format() == 'channels_first' else -1\n",
    "    # Depth\n",
    "    tchannel = tf.keras.backend.int_shape(x)[channel_axis] * 1\n",
    "    # Width\n",
    "    cchannel = int(16 * 1)\n",
    "    x = Conv2D(tchannel, (1,1), padding='same', strides=(1,1),name='conv4')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name='BN4')(x)\n",
    "    x = Activation(swish)(x)\n",
    "    \n",
    "    x = DepthwiseConv2D((3,3), strides=(1, 1), depth_multiplier=1, padding='same',name = 'conv5')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN5')(x)\n",
    "    x = Activation(swish)(x)   \n",
    "    x = Conv2D(cchannel, (1,1), padding='same', strides=(1,1),name='conv6')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN6')(x)\n",
    "    x = Add()([x, inputs])\n",
    "    '''_inverted_residual_block stage1'''\n",
    "    \n",
    "    x = Conv2D(16, (2,2), padding='same', strides=(2,2),name='conv7')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name='BN7')(x)\n",
    "    x = Activation(swish)(x)\n",
    "    \n",
    "    '''_inverted_residual_block stage2'''\n",
    "    \n",
    "    inputs = x\n",
    "    channel_axis = 1 if tf.keras.backend.image_data_format() == 'channels_first' else -1\n",
    "    # Depth\n",
    "    tchannel = tf.keras.backend.int_shape(x)[channel_axis] * 6\n",
    "    # Width\n",
    "    cchannel = int(32 * 1)\n",
    "    x = Conv2D(tchannel, (1,1), padding='same', strides=(1,1),name='conv8')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name='BN8')(x)\n",
    "    x = Activation(swish)(x)\n",
    "    \n",
    "    x = DepthwiseConv2D((3,3), strides=(1, 1), depth_multiplier=1, padding='same',name = 'conv9')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN9')(x)\n",
    "    x = Activation(swish)(x)   \n",
    "    x = Conv2D(cchannel, (1,1), padding='same', strides=(1,1),name='conv10')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN10')(x)\n",
    "    #x = Add()([x, inputs])\n",
    "    \n",
    "    ###################################\n",
    "    \n",
    "    inputs = x\n",
    "    channel_axis = 1 if tf.keras.backend.image_data_format() == 'channels_first' else -1\n",
    "    # Depth\n",
    "    tchannel = tf.keras.backend.int_shape(x)[channel_axis] * 6\n",
    "    # Width\n",
    "    cchannel = int(32 * 1)\n",
    "    x = Conv2D(tchannel, (1,1), padding='same', strides=(1,1),name='conv11')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name='BN11')(x)\n",
    "    x = Activation(swish)(x)\n",
    "    \n",
    "    x = DepthwiseConv2D((3,3), strides=(1, 1), depth_multiplier=1, padding='same',name = 'conv12')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN12')(x)\n",
    "    x = Activation(swish)(x)   \n",
    "    x = Conv2D(cchannel, (1,1), padding='same', strides=(1,1),name='conv13')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN13')(x)\n",
    "    x = Add()([x, inputs])\n",
    "    \n",
    "    inputs = x\n",
    "    channel_axis = 1 if tf.keras.backend.image_data_format() == 'channels_first' else -1\n",
    "    # Depth\n",
    "    tchannel = tf.keras.backend.int_shape(x)[channel_axis] * 6\n",
    "    # Width\n",
    "    cchannel = int(32 * 1)\n",
    "    x = Conv2D(tchannel, (1,1), padding='same', strides=(1,1),name='conv14')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name='BN14')(x)\n",
    "    x = Activation(swish)(x)\n",
    "    \n",
    "    x = DepthwiseConv2D((3,3), strides=(1, 1), depth_multiplier=1, padding='same',name = 'conv15')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN15')(x)\n",
    "    x = Activation(swish)(x)   \n",
    "    x = Conv2D(cchannel, (1,1), padding='same', strides=(1,1),name='conv16')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN16')(x)\n",
    "    x = Add()([x, inputs])\n",
    "    \n",
    "    '''_inverted_residual_block stage2'''\n",
    "    \n",
    "    x = Conv2D(32, (2,2), padding='same', strides=(2,2),name='conv17')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name='BN17')(x)\n",
    "    x = Activation(swish)(x)\n",
    "    \n",
    "    '''_inverted_residual_block stage3'''\n",
    "    #1\n",
    "    inputs = x\n",
    "    channel_axis = 1 if tf.keras.backend.image_data_format() == 'channels_first' else -1\n",
    "    # Depth\n",
    "    tchannel = tf.keras.backend.int_shape(x)[channel_axis] * 6\n",
    "    # Width\n",
    "    cchannel = int(64 * 1)\n",
    "    x = Conv2D(tchannel, (1,1), padding='same', strides=(1,1),name='conv18')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name='BN18')(x)\n",
    "    x = Activation(swish)(x)\n",
    "    \n",
    "    x = DepthwiseConv2D((3,3), strides=(1, 1), depth_multiplier=1, padding='same',name = 'conv19')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN19')(x)\n",
    "    x = Activation(swish)(x)   \n",
    "    x = Conv2D(cchannel, (1,1), padding='same', strides=(1,1),name='conv20')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN20')(x)\n",
    "    #x = Add()([x, inputs])\n",
    "    \n",
    "    ###################################\n",
    "    #2\n",
    "    inputs = x\n",
    "    channel_axis = 1 if tf.keras.backend.image_data_format() == 'channels_first' else -1\n",
    "    # Depth\n",
    "    tchannel = tf.keras.backend.int_shape(x)[channel_axis] * 6\n",
    "    # Width\n",
    "    cchannel = int(64 * 1)\n",
    "    x = Conv2D(tchannel, (1,1), padding='same', strides=(1,1),name='conv21')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name='BN22')(x)\n",
    "    x = Activation(swish)(x)\n",
    "    \n",
    "    x = DepthwiseConv2D((3,3), strides=(1, 1), depth_multiplier=1, padding='same',name = 'conv23')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN23')(x)\n",
    "    x = Activation(swish)(x)   \n",
    "    x = Conv2D(cchannel, (1,1), padding='same', strides=(1,1),name='conv24')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN24')(x)\n",
    "    x = Add()([x, inputs])\n",
    "    #3\n",
    "    inputs = x\n",
    "    channel_axis = 1 if tf.keras.backend.image_data_format() == 'channels_first' else -1\n",
    "    # Depth\n",
    "    tchannel = tf.keras.backend.int_shape(x)[channel_axis] * 6\n",
    "    # Width\n",
    "    cchannel = int(64 * 1)\n",
    "    x = Conv2D(tchannel, (1,1), padding='same', strides=(1,1),name='conv25')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name='BN25')(x)\n",
    "    x = Activation(swish)(x)\n",
    "    \n",
    "    x = DepthwiseConv2D((3,3), strides=(1, 1), depth_multiplier=1, padding='same',name = 'conv26')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN26')(x)\n",
    "    x = Activation(swish)(x)   \n",
    "    x = Conv2D(cchannel, (1,1), padding='same', strides=(1,1),name='conv27')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN27')(x)\n",
    "    x = Add()([x, inputs])\n",
    "    #4\n",
    "    inputs = x\n",
    "    channel_axis = 1 if tf.keras.backend.image_data_format() == 'channels_first' else -1\n",
    "    # Depth\n",
    "    tchannel = tf.keras.backend.int_shape(x)[channel_axis] * 6\n",
    "    # Width\n",
    "    cchannel = int(64 * 1)\n",
    "    x = Conv2D(tchannel, (1,1), padding='same', strides=(1,1),name='conv28')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name='BN28')(x)\n",
    "    x = Activation(swish)(x)\n",
    "    \n",
    "    x = DepthwiseConv2D((3,3), strides=(1, 1), depth_multiplier=1, padding='same',name = 'conv29')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN29')(x)\n",
    "    x = Activation(swish)(x)   \n",
    "    x = Conv2D(cchannel, (1,1), padding='same', strides=(1,1),name='conv30')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN30')(x)\n",
    "    x = Add()([x, inputs])\n",
    "    \n",
    "    branch_2 = x\n",
    "    branch_3 = x\n",
    "    branch_4 = x\n",
    "    branch_tt = x\n",
    "    '''_inverted_residual_block stage3'''\n",
    "    \n",
    "    x = Conv2D(64, (2,2), padding='same', strides=(2,2),name='conv31')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name='BN31')(x)\n",
    "    x = Activation(swish)(x)\n",
    "    \n",
    "    '''branch_tt'''\n",
    "\n",
    "    \n",
    "    '''branch_2'''\n",
    "    channel_axis = 1 if tf.keras.backend.image_data_format() == 'channels_first' else -1\n",
    "    # Depth\n",
    "    tchannel = tf.keras.backend.int_shape(branch_2)[channel_axis] * 6\n",
    "    # Width\n",
    "    cchannel = int(96 * 1)\n",
    "    branch_2 = Conv2D(tchannel, (1,1), padding='same', strides=(1,1),name='b2_conv0')(branch_2)\n",
    "    branch_2 = BatchNormalization(axis=channel_axis,name='b2_BN0')(branch_2)\n",
    "    branch_2 = Activation(swish)(branch_2)\n",
    "    \n",
    "    branch_2 = DepthwiseConv2D((3,3), strides=(1, 1), depth_multiplier=1, padding='same',name = 'b2_conv1')(branch_2)\n",
    "    branch_2 = BatchNormalization(axis=channel_axis,name = 'b2_BN1')(branch_2)\n",
    "    branch_2 = Activation(swish)(branch_2)   \n",
    "    branch_2 = Conv2D(cchannel, (1,1), padding='same', strides=(1,1),name='b2_conv2')(branch_2)\n",
    "    branch_2 = BatchNormalization(axis=channel_axis,name = 'b2_BN2')(branch_2)\n",
    "    \n",
    "    branch_2 = Conv2D(320, (1,1), padding='same', strides=(1,1),name='b2_conv3')(branch_2)\n",
    "    branch_2 = BatchNormalization(axis=channel_axis,name='b2_BN3')(branch_2)\n",
    "    branch_2 = Activation(swish)(branch_2)\n",
    "    branch_2 = GlobalAveragePooling2D()(branch_2)\n",
    "    branch_2 = Reshape((1, 1, 320))(branch_2)\n",
    "    branch_2 = Dropout(0.3, name='b2_Dropout')(branch_2)\n",
    "    branch_2 = Conv2D(2, (1, 1), padding='same',name = 'b2_conv4')(branch_2)\n",
    "    branch_2 = Activation('softmax', name='b2_softmax')(branch_2)\n",
    "    b2_output = Reshape((2,))(branch_2)\n",
    "    '''\n",
    "    branch_2 = Conv2D(128, (1,1), padding='same', strides=(1,1),name='b2_conv0')(branch_2)\n",
    "    branch_2 = BatchNormalization(axis=channel_axis,name='b2_BN0')(branch_2)\n",
    "    branch_2 = Activation(swish)(branch_2)\n",
    "    \n",
    "    branch_2 = Conv2D(128, (2,2), padding='same', strides=(2,2),name='b2_conv1')(branch_2)\n",
    "    branch_2 = BatchNormalization(axis=channel_axis,name='b2_BN1')(branch_2)\n",
    "    branch_2 = Activation(swish)(branch_2)\n",
    "    \n",
    "    branch_2 = Conv2D(320, (1,1), padding='same', strides=(1,1),name='b2_conv2')(branch_2)\n",
    "    branch_2 = BatchNormalization(axis=channel_axis,name='b2_BN2')(branch_2)\n",
    "    branch_2 = Activation(swish)(branch_2)\n",
    "    branch_2 = GlobalAveragePooling2D()(branch_2)\n",
    "    branch_2 = Reshape((1, 1, 320))(branch_2)\n",
    "    branch_2 = Dropout(0.3, name='b2_Dropout')(branch_2)\n",
    "    branch_2 = Conv2D(2, (1, 1), padding='same',name = 'b2_conv3')(branch_2)\n",
    "    branch_2 = Activation('softmax', name='b2_softmax')(branch_2)\n",
    "    b2_output = Reshape((2,))(branch_2)\n",
    "    '''\n",
    "    \n",
    "\n",
    "    \n",
    "    '''branch_2'''\n",
    "\n",
    "\n",
    "    '''branch_3'''\n",
    "    channel_axis = 1 if tf.keras.backend.image_data_format() == 'channels_first' else -1\n",
    "    # Depth\n",
    "    tchannel = tf.keras.backend.int_shape(branch_3)[channel_axis] * 6\n",
    "    # Width\n",
    "    cchannel = int(96 * 1)\n",
    "    branch_3 = Conv2D(tchannel, (1,1), padding='same', strides=(1,1),name='b3_conv0')(branch_3)\n",
    "    branch_3 = BatchNormalization(axis=channel_axis,name='b3_BN0')(branch_3)\n",
    "    branch_3 = Activation(swish)(branch_3)\n",
    "    \n",
    "    branch_3 = DepthwiseConv2D((3,3), strides=(1, 1), depth_multiplier=1, padding='same',name = 'b3_conv1')(branch_3)\n",
    "    branch_3 = BatchNormalization(axis=channel_axis,name = 'b3_BN1')(branch_3)\n",
    "    branch_3 = Activation(swish)(branch_3)   \n",
    "    branch_3 = Conv2D(cchannel, (1,1), padding='same', strides=(1,1),name='b3_conv2')(branch_3)\n",
    "    branch_3 = BatchNormalization(axis=channel_axis,name = 'b3_BN2')(branch_3)\n",
    "    \n",
    "    branch_3 = Conv2D(320, (1,1), padding='same', strides=(1,1),name='b3_conv3')(branch_3)\n",
    "    branch_3 = BatchNormalization(axis=channel_axis,name='b3_BN3')(branch_3)\n",
    "    branch_3 = Activation(swish)(branch_3)\n",
    "    branch_3 = GlobalAveragePooling2D()(branch_3)\n",
    "    branch_3 = Reshape((1, 1, 320))(branch_3)\n",
    "    branch_3 = Dropout(0.3, name='b3_Dropout')(branch_3)\n",
    "    branch_3 = Conv2D(2, (1, 1), padding='same',name = 'b3_conv4')(branch_3)\n",
    "    branch_3 = Activation('softmax', name='b3_softmax')(branch_3)\n",
    "    b3_output = Reshape((2,))(branch_3)\n",
    "    '''\n",
    "    branch_3 = Conv2D(128, (1,1), padding='same', strides=(1,1),name='b3_conv0')(branch_3)\n",
    "    branch_3 = BatchNormalization(axis=channel_axis,name='b3_BN0')(branch_3)\n",
    "    branch_3 = Activation(swish)(branch_3)\n",
    "    \n",
    "    branch_3 = Conv2D(128, (2,2), padding='same', strides=(2,2),name='b3_conv1')(branch_3)\n",
    "    branch_3 = BatchNormalization(axis=channel_axis,name='b3_BN1')(branch_3)\n",
    "    branch_3 = Activation(swish)(branch_3)\n",
    "    \n",
    "    branch_3 = Conv2D(320, (1,1), padding='same', strides=(1,1),name='b3_conv2')(branch_3)\n",
    "    branch_3 = BatchNormalization(axis=channel_axis,name='b3_BN2')(branch_3)\n",
    "    branch_3 = Activation(swish)(branch_3)\n",
    "    branch_3 = GlobalAveragePooling2D()(branch_3)\n",
    "    branch_3 = Reshape((1, 1, 320))(branch_3)\n",
    "    branch_3 = Dropout(0.3, name='b3_Dropout')(branch_3)\n",
    "    branch_3 = Conv2D(2, (1, 1), padding='same',name = 'b3_conv3')(branch_3)\n",
    "    branch_3 = Activation('softmax', name='b3_softmax')(branch_3)\n",
    "    b3_output = Reshape((2,))(branch_3)\n",
    "    '''\n",
    "    '''branch_3'''\n",
    "    \n",
    "    '''bracnch4'''\n",
    "    channel_axis = 1 if tf.keras.backend.image_data_format() == 'channels_first' else -1\n",
    "    # Depth\n",
    "    tchannel = tf.keras.backend.int_shape(x)[channel_axis] * 6\n",
    "    # Width\n",
    "    cchannel = int(96 * 1)\n",
    "    branch_4 = Conv2D(tchannel, (1,1), padding='same', strides=(1,1),name='b4_conv0')(branch_4)\n",
    "    branch_4 = BatchNormalization(axis=channel_axis,name='b4_BN0')(branch_4)\n",
    "    branch_4 = Activation(swish)(branch_4)\n",
    "    \n",
    "    branch_4 = DepthwiseConv2D((3,3), strides=(1, 1), depth_multiplier=1, padding='same',name = 'b4_conv1')(branch_4)\n",
    "    branch_4 = BatchNormalization(axis=channel_axis,name = 'b4_BN1')(branch_4)\n",
    "    branch_4 = Activation(swish)(branch_4)   \n",
    "    branch_4 = Conv2D(cchannel, (1,1), padding='same', strides=(1,1),name='b4_conv2')(branch_4)\n",
    "    branch_4 = BatchNormalization(axis=channel_axis,name = 'b4_BN2')(branch_4)\n",
    "    \n",
    "    branch_4 = Conv2D(320, (1,1), padding='same', strides=(1,1),name='b4_conv3')(branch_4)\n",
    "    branch_4 = BatchNormalization(axis=channel_axis,name='b4_BN3')(branch_4)\n",
    "    branch_4 = Activation(swish)(branch_4)\n",
    "    branch_4 = GlobalAveragePooling2D()(branch_4)\n",
    "    branch_4 = Reshape((1, 1, 320))(branch_4)\n",
    "    branch_4 = Dropout(0.3, name='b4_Dropout')(branch_4)\n",
    "    branch_4 = Conv2D(2, (1, 1), padding='same',name = 'b4_conv4')(branch_4)\n",
    "    branch_4 = Activation('softmax', name='b4_softmax')(branch_4)\n",
    "    b4_output = Reshape((2,))(branch_4)\n",
    "    '''\n",
    "    branch_4 = Conv2D(128, (1,1), padding='same', strides=(1,1),name='b4_conv0')(branch_4)\n",
    "    branch_4 = BatchNormalization(axis=channel_axis,name='b4_BN0')(branch_4)\n",
    "    branch_4 = Activation(swish)(branch_4)\n",
    "    \n",
    "    branch_4 = Conv2D(128, (2,2), padding='same', strides=(2,2),name='b4_conv1')(branch_4)\n",
    "    branch_4 = BatchNormalization(axis=channel_axis,name='b4_BN1')(branch_4)\n",
    "    branch_4 = Activation(swish)(branch_4)\n",
    "    \n",
    "    branch_4 = Conv2D(320, (1,1), padding='same', strides=(1,1),name='b4_conv2')(branch_4)\n",
    "    branch_4 = BatchNormalization(axis=channel_axis,name='b4_BN2')(branch_4)\n",
    "    branch_4 = Activation(swish)(branch_4)\n",
    "    branch_4 = GlobalAveragePooling2D()(branch_4)\n",
    "    branch_4 = Reshape((1, 1, 320))(branch_4)\n",
    "    branch_4 = Dropout(0.3, name='b4_Dropout')(branch_4)\n",
    "    branch_4 = Conv2D(2, (1, 1), padding='same',name = 'b4_conv3')(branch_4)\n",
    "    branch_4 = Activation('softmax', name='b4_softmax')(branch_4)\n",
    "    b4_output = Reshape((2,))(branch_4)\n",
    "    '''\n",
    "    \n",
    "    channel_axis = 1 if tf.keras.backend.image_data_format() == 'channels_first' else -1\n",
    "    # Depth\n",
    "    tchannel = tf.keras.backend.int_shape(branch_tt)[channel_axis] * 6\n",
    "    # Width\n",
    "    cchannel = int(96 * 1)\n",
    "    branch_tt = Conv2D(tchannel, (1,1), padding='same', strides=(1,1),name='b5_conv0')(branch_tt)\n",
    "    branch_tt = BatchNormalization(axis=channel_axis,name='b5_BN0')(branch_tt)\n",
    "    branch_tt = Activation(swish)(branch_tt)\n",
    "    \n",
    "    branch_tt = DepthwiseConv2D((3,3), strides=(1, 1), depth_multiplier=1, padding='same',name = 'b5_conv1')(branch_tt)\n",
    "    branch_tt = BatchNormalization(axis=channel_axis,name = 'b5_BN1')(branch_tt)\n",
    "    branch_tt = Activation(swish)(branch_tt)   \n",
    "    branch_tt = Conv2D(cchannel, (1,1), padding='same', strides=(1,1),name='b5_conv2')(branch_tt)\n",
    "    branch_tt = BatchNormalization(axis=channel_axis,name = 'b5_BN2')(branch_tt)\n",
    "    \n",
    "    branch_tt = Conv2D(320, (1,1), padding='same', strides=(1,1),name='b5_conv3')(branch_tt)\n",
    "    branch_tt = BatchNormalization(axis=channel_axis,name='b5_BN3')(branch_tt)\n",
    "    branch_tt = Activation(swish)(branch_tt)\n",
    "    branch_tt = GlobalAveragePooling2D()(branch_tt)\n",
    "    branch_tt = Reshape((1, 1, 320))(branch_tt)\n",
    "    branch_tt = Dropout(0.3, name='b5_Dropout')(branch_tt)\n",
    "    branch_tt = Conv2D(2, (1, 1), padding='same',name = 'b5_conv4')(branch_tt)\n",
    "    branch_tt = Activation('softmax', name='b5_softmax')(branch_tt)\n",
    "    b5_output = Reshape((2,))(branch_tt)\n",
    "    \n",
    "    '''_inverted_residual_block stage4'''\n",
    "    #1\n",
    "    inputs = x\n",
    "    channel_axis = 1 if tf.keras.backend.image_data_format() == 'channels_first' else -1\n",
    "    # Depth\n",
    "    tchannel = tf.keras.backend.int_shape(x)[channel_axis] * 6\n",
    "    # Width\n",
    "    cchannel = int(96 * 1)\n",
    "    x = Conv2D(tchannel, (1,1), padding='same', strides=(1,1),name='conv32')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name='BN32')(x)\n",
    "    x = Activation(swish)(x)\n",
    "    \n",
    "    x = DepthwiseConv2D((3,3), strides=(1, 1), depth_multiplier=1, padding='same',name = 'conv33')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN33')(x)\n",
    "    x = Activation(swish)(x)   \n",
    "    x = Conv2D(cchannel, (1,1), padding='same', strides=(1,1),name='conv34')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN34')(x)\n",
    "    #x = Add()([x, inputs])\n",
    "    ###################################\n",
    "    #2\n",
    "    inputs = x\n",
    "    channel_axis = 1 if tf.keras.backend.image_data_format() == 'channels_first' else -1\n",
    "    # Depth\n",
    "    tchannel = tf.keras.backend.int_shape(x)[channel_axis] * 6\n",
    "    # Width\n",
    "    cchannel = int(96 * 1)\n",
    "    x = Conv2D(tchannel, (1,1), padding='same', strides=(1,1),name='conv35')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name='BN35')(x)\n",
    "    x = Activation(swish)(x)\n",
    "    \n",
    "    x = DepthwiseConv2D((3,3), strides=(1, 1), depth_multiplier=1, padding='same',name = 'conv36')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN36')(x)\n",
    "    x = Activation(swish)(x)   \n",
    "    x = Conv2D(cchannel, (1,1), padding='same', strides=(1,1),name='conv37')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name = 'BN37')(x)\n",
    "    x = Add()([x, inputs])\n",
    "    \n",
    "    '''_inverted_residual_block stage4'''\n",
    "    \n",
    "    '''Final Stage'''\n",
    "    \n",
    "    if alpha > 1.0:\n",
    "        last_filters = _make_divisible(320 * alpha, 8)\n",
    "    else:\n",
    "        last_filters = 320\n",
    "\n",
    "        \n",
    "    x = Conv2D(320, (1,1), padding='same', strides=(1,1),name='conv38')(x)\n",
    "    x = BatchNormalization(axis=channel_axis,name='BN38')(x)\n",
    "    x = Activation(swish)(x)\n",
    "    x = GlobalAveragePooling2D()(x)\n",
    "    x = Reshape((1, 1, last_filters))(x)\n",
    "    x = Dropout(0.3, name='Dropout')(x)\n",
    "    x = Conv2D(k, (1, 1), padding='same',name = 'conv39')(x)\n",
    "    x = Activation('softmax', name='softmax')(x)\n",
    "    output = Reshape((k,))(x)\n",
    "\n",
    "    model = Model(inputs_img,[output,b2_output,b3_output,b4_output,b5_output])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = MobileNetv2((32, 32, 3), 3, 1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "Input0 (InputLayer)             [(None, 32, 32, 3)]  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv0 (Conv2D)                  (None, 32, 32, 16)   448         Input0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "BN0 (BatchNormalization)        (None, 32, 32, 16)   64          conv0[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation (Activation)         (None, 32, 32, 16)   0           BN0[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "conv1 (Conv2D)                  (None, 32, 32, 16)   272         activation[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "BN1 (BatchNormalization)        (None, 32, 32, 16)   64          conv1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, 32, 32, 16)   0           BN1[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "conv2 (DepthwiseConv2D)         (None, 32, 32, 16)   160         activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "BN2 (BatchNormalization)        (None, 32, 32, 16)   64          conv2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, 32, 32, 16)   0           BN2[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "conv3 (Conv2D)                  (None, 32, 32, 16)   272         activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "BN3 (BatchNormalization)        (None, 32, 32, 16)   64          conv3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv4 (Conv2D)                  (None, 32, 32, 16)   272         BN3[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "BN4 (BatchNormalization)        (None, 32, 32, 16)   64          conv4[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_3 (Activation)       (None, 32, 32, 16)   0           BN4[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "conv5 (DepthwiseConv2D)         (None, 32, 32, 16)   160         activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "BN5 (BatchNormalization)        (None, 32, 32, 16)   64          conv5[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, 32, 32, 16)   0           BN5[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "conv6 (Conv2D)                  (None, 32, 32, 16)   272         activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "BN6 (BatchNormalization)        (None, 32, 32, 16)   64          conv6[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "add (Add)                       (None, 32, 32, 16)   0           BN6[0][0]                        \n",
      "                                                                 BN3[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "conv7 (Conv2D)                  (None, 16, 16, 16)   1040        add[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "BN7 (BatchNormalization)        (None, 16, 16, 16)   64          conv7[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, 16, 16, 16)   0           BN7[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "conv8 (Conv2D)                  (None, 16, 16, 96)   1632        activation_5[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "BN8 (BatchNormalization)        (None, 16, 16, 96)   384         conv8[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_6 (Activation)       (None, 16, 16, 96)   0           BN8[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "conv9 (DepthwiseConv2D)         (None, 16, 16, 96)   960         activation_6[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "BN9 (BatchNormalization)        (None, 16, 16, 96)   384         conv9[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_7 (Activation)       (None, 16, 16, 96)   0           BN9[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "conv10 (Conv2D)                 (None, 16, 16, 32)   3104        activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "BN10 (BatchNormalization)       (None, 16, 16, 32)   128         conv10[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv11 (Conv2D)                 (None, 16, 16, 192)  6336        BN10[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "BN11 (BatchNormalization)       (None, 16, 16, 192)  768         conv11[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_8 (Activation)       (None, 16, 16, 192)  0           BN11[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "conv12 (DepthwiseConv2D)        (None, 16, 16, 192)  1920        activation_8[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "BN12 (BatchNormalization)       (None, 16, 16, 192)  768         conv12[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_9 (Activation)       (None, 16, 16, 192)  0           BN12[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "conv13 (Conv2D)                 (None, 16, 16, 32)   6176        activation_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "BN13 (BatchNormalization)       (None, 16, 16, 32)   128         conv13[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "add_1 (Add)                     (None, 16, 16, 32)   0           BN13[0][0]                       \n",
      "                                                                 BN10[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "conv14 (Conv2D)                 (None, 16, 16, 192)  6336        add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "BN14 (BatchNormalization)       (None, 16, 16, 192)  768         conv14[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_10 (Activation)      (None, 16, 16, 192)  0           BN14[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "conv15 (DepthwiseConv2D)        (None, 16, 16, 192)  1920        activation_10[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "BN15 (BatchNormalization)       (None, 16, 16, 192)  768         conv15[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_11 (Activation)      (None, 16, 16, 192)  0           BN15[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "conv16 (Conv2D)                 (None, 16, 16, 32)   6176        activation_11[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "BN16 (BatchNormalization)       (None, 16, 16, 32)   128         conv16[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "add_2 (Add)                     (None, 16, 16, 32)   0           BN16[0][0]                       \n",
      "                                                                 add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv17 (Conv2D)                 (None, 8, 8, 32)     4128        add_2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "BN17 (BatchNormalization)       (None, 8, 8, 32)     128         conv17[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_12 (Activation)      (None, 8, 8, 32)     0           BN17[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "conv18 (Conv2D)                 (None, 8, 8, 192)    6336        activation_12[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "BN18 (BatchNormalization)       (None, 8, 8, 192)    768         conv18[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_13 (Activation)      (None, 8, 8, 192)    0           BN18[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "conv19 (DepthwiseConv2D)        (None, 8, 8, 192)    1920        activation_13[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "BN19 (BatchNormalization)       (None, 8, 8, 192)    768         conv19[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_14 (Activation)      (None, 8, 8, 192)    0           BN19[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "conv20 (Conv2D)                 (None, 8, 8, 64)     12352       activation_14[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "BN20 (BatchNormalization)       (None, 8, 8, 64)     256         conv20[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv21 (Conv2D)                 (None, 8, 8, 384)    24960       BN20[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "BN22 (BatchNormalization)       (None, 8, 8, 384)    1536        conv21[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_15 (Activation)      (None, 8, 8, 384)    0           BN22[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "conv23 (DepthwiseConv2D)        (None, 8, 8, 384)    3840        activation_15[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "BN23 (BatchNormalization)       (None, 8, 8, 384)    1536        conv23[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_16 (Activation)      (None, 8, 8, 384)    0           BN23[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "conv24 (Conv2D)                 (None, 8, 8, 64)     24640       activation_16[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "BN24 (BatchNormalization)       (None, 8, 8, 64)     256         conv24[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "add_3 (Add)                     (None, 8, 8, 64)     0           BN24[0][0]                       \n",
      "                                                                 BN20[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "conv25 (Conv2D)                 (None, 8, 8, 384)    24960       add_3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "BN25 (BatchNormalization)       (None, 8, 8, 384)    1536        conv25[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_17 (Activation)      (None, 8, 8, 384)    0           BN25[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "conv26 (DepthwiseConv2D)        (None, 8, 8, 384)    3840        activation_17[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "BN26 (BatchNormalization)       (None, 8, 8, 384)    1536        conv26[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_18 (Activation)      (None, 8, 8, 384)    0           BN26[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "conv27 (Conv2D)                 (None, 8, 8, 64)     24640       activation_18[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "BN27 (BatchNormalization)       (None, 8, 8, 64)     256         conv27[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "add_4 (Add)                     (None, 8, 8, 64)     0           BN27[0][0]                       \n",
      "                                                                 add_3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv28 (Conv2D)                 (None, 8, 8, 384)    24960       add_4[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "BN28 (BatchNormalization)       (None, 8, 8, 384)    1536        conv28[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_19 (Activation)      (None, 8, 8, 384)    0           BN28[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "conv29 (DepthwiseConv2D)        (None, 8, 8, 384)    3840        activation_19[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "BN29 (BatchNormalization)       (None, 8, 8, 384)    1536        conv29[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_20 (Activation)      (None, 8, 8, 384)    0           BN29[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "conv30 (Conv2D)                 (None, 8, 8, 64)     24640       activation_20[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "BN30 (BatchNormalization)       (None, 8, 8, 64)     256         conv30[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "add_5 (Add)                     (None, 8, 8, 64)     0           BN30[0][0]                       \n",
      "                                                                 add_4[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv31 (Conv2D)                 (None, 4, 4, 64)     16448       add_5[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "BN31 (BatchNormalization)       (None, 4, 4, 64)     256         conv31[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_21 (Activation)      (None, 4, 4, 64)     0           BN31[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "conv32 (Conv2D)                 (None, 4, 4, 384)    24960       activation_21[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "BN32 (BatchNormalization)       (None, 4, 4, 384)    1536        conv32[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_34 (Activation)      (None, 4, 4, 384)    0           BN32[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "conv33 (DepthwiseConv2D)        (None, 4, 4, 384)    3840        activation_34[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "BN33 (BatchNormalization)       (None, 4, 4, 384)    1536        conv33[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_35 (Activation)      (None, 4, 4, 384)    0           BN33[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "conv34 (Conv2D)                 (None, 4, 4, 96)     36960       activation_35[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "BN34 (BatchNormalization)       (None, 4, 4, 96)     384         conv34[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv35 (Conv2D)                 (None, 4, 4, 576)    55872       BN34[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "BN35 (BatchNormalization)       (None, 4, 4, 576)    2304        conv35[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "b2_conv0 (Conv2D)               (None, 8, 8, 384)    24960       add_5[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "b3_conv0 (Conv2D)               (None, 8, 8, 384)    24960       add_5[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "b4_conv0 (Conv2D)               (None, 8, 8, 384)    24960       add_5[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "b5_conv0 (Conv2D)               (None, 8, 8, 384)    24960       add_5[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_36 (Activation)      (None, 4, 4, 576)    0           BN35[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "b2_BN0 (BatchNormalization)     (None, 8, 8, 384)    1536        b2_conv0[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "b3_BN0 (BatchNormalization)     (None, 8, 8, 384)    1536        b3_conv0[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "b4_BN0 (BatchNormalization)     (None, 8, 8, 384)    1536        b4_conv0[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "b5_BN0 (BatchNormalization)     (None, 8, 8, 384)    1536        b5_conv0[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv36 (DepthwiseConv2D)        (None, 4, 4, 576)    5760        activation_36[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_22 (Activation)      (None, 8, 8, 384)    0           b2_BN0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_25 (Activation)      (None, 8, 8, 384)    0           b3_BN0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_28 (Activation)      (None, 8, 8, 384)    0           b4_BN0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_31 (Activation)      (None, 8, 8, 384)    0           b5_BN0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "BN36 (BatchNormalization)       (None, 4, 4, 576)    2304        conv36[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "b2_conv1 (DepthwiseConv2D)      (None, 8, 8, 384)    3840        activation_22[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "b3_conv1 (DepthwiseConv2D)      (None, 8, 8, 384)    3840        activation_25[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "b4_conv1 (DepthwiseConv2D)      (None, 8, 8, 384)    3840        activation_28[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "b5_conv1 (DepthwiseConv2D)      (None, 8, 8, 384)    3840        activation_31[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_37 (Activation)      (None, 4, 4, 576)    0           BN36[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "b2_BN1 (BatchNormalization)     (None, 8, 8, 384)    1536        b2_conv1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "b3_BN1 (BatchNormalization)     (None, 8, 8, 384)    1536        b3_conv1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "b4_BN1 (BatchNormalization)     (None, 8, 8, 384)    1536        b4_conv1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "b5_BN1 (BatchNormalization)     (None, 8, 8, 384)    1536        b5_conv1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv37 (Conv2D)                 (None, 4, 4, 96)     55392       activation_37[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_23 (Activation)      (None, 8, 8, 384)    0           b2_BN1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_26 (Activation)      (None, 8, 8, 384)    0           b3_BN1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_29 (Activation)      (None, 8, 8, 384)    0           b4_BN1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_32 (Activation)      (None, 8, 8, 384)    0           b5_BN1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "BN37 (BatchNormalization)       (None, 4, 4, 96)     384         conv37[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "b2_conv2 (Conv2D)               (None, 8, 8, 96)     36960       activation_23[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "b3_conv2 (Conv2D)               (None, 8, 8, 96)     36960       activation_26[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "b4_conv2 (Conv2D)               (None, 8, 8, 96)     36960       activation_29[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "b5_conv2 (Conv2D)               (None, 8, 8, 96)     36960       activation_32[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_6 (Add)                     (None, 4, 4, 96)     0           BN37[0][0]                       \n",
      "                                                                 BN34[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "b2_BN2 (BatchNormalization)     (None, 8, 8, 96)     384         b2_conv2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "b3_BN2 (BatchNormalization)     (None, 8, 8, 96)     384         b3_conv2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "b4_BN2 (BatchNormalization)     (None, 8, 8, 96)     384         b4_conv2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "b5_BN2 (BatchNormalization)     (None, 8, 8, 96)     384         b5_conv2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv38 (Conv2D)                 (None, 4, 4, 320)    31040       add_6[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "b2_conv3 (Conv2D)               (None, 8, 8, 320)    31040       b2_BN2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "b3_conv3 (Conv2D)               (None, 8, 8, 320)    31040       b3_BN2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "b4_conv3 (Conv2D)               (None, 8, 8, 320)    31040       b4_BN2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "b5_conv3 (Conv2D)               (None, 8, 8, 320)    31040       b5_BN2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "BN38 (BatchNormalization)       (None, 4, 4, 320)    1280        conv38[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "b2_BN3 (BatchNormalization)     (None, 8, 8, 320)    1280        b2_conv3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "b3_BN3 (BatchNormalization)     (None, 8, 8, 320)    1280        b3_conv3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "b4_BN3 (BatchNormalization)     (None, 8, 8, 320)    1280        b4_conv3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "b5_BN3 (BatchNormalization)     (None, 8, 8, 320)    1280        b5_conv3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_38 (Activation)      (None, 4, 4, 320)    0           BN38[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "activation_24 (Activation)      (None, 8, 8, 320)    0           b2_BN3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_27 (Activation)      (None, 8, 8, 320)    0           b3_BN3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_30 (Activation)      (None, 8, 8, 320)    0           b4_BN3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_33 (Activation)      (None, 8, 8, 320)    0           b5_BN3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling2d_4 (Glo (None, 320)          0           activation_38[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling2d (Globa (None, 320)          0           activation_24[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling2d_1 (Glo (None, 320)          0           activation_27[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling2d_2 (Glo (None, 320)          0           activation_30[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling2d_3 (Glo (None, 320)          0           activation_33[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "reshape_8 (Reshape)             (None, 1, 1, 320)    0           global_average_pooling2d_4[0][0] \n",
      "__________________________________________________________________________________________________\n",
      "reshape (Reshape)               (None, 1, 1, 320)    0           global_average_pooling2d[0][0]   \n",
      "__________________________________________________________________________________________________\n",
      "reshape_2 (Reshape)             (None, 1, 1, 320)    0           global_average_pooling2d_1[0][0] \n",
      "__________________________________________________________________________________________________\n",
      "reshape_4 (Reshape)             (None, 1, 1, 320)    0           global_average_pooling2d_2[0][0] \n",
      "__________________________________________________________________________________________________\n",
      "reshape_6 (Reshape)             (None, 1, 1, 320)    0           global_average_pooling2d_3[0][0] \n",
      "__________________________________________________________________________________________________\n",
      "Dropout (Dropout)               (None, 1, 1, 320)    0           reshape_8[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "b2_Dropout (Dropout)            (None, 1, 1, 320)    0           reshape[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "b3_Dropout (Dropout)            (None, 1, 1, 320)    0           reshape_2[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "b4_Dropout (Dropout)            (None, 1, 1, 320)    0           reshape_4[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "b5_Dropout (Dropout)            (None, 1, 1, 320)    0           reshape_6[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv39 (Conv2D)                 (None, 1, 1, 3)      963         Dropout[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "b2_conv4 (Conv2D)               (None, 1, 1, 2)      642         b2_Dropout[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "b3_conv4 (Conv2D)               (None, 1, 1, 2)      642         b3_Dropout[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "b4_conv4 (Conv2D)               (None, 1, 1, 2)      642         b4_Dropout[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "b5_conv4 (Conv2D)               (None, 1, 1, 2)      642         b5_Dropout[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "softmax (Activation)            (None, 1, 1, 3)      0           conv39[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "b2_softmax (Activation)         (None, 1, 1, 2)      0           b2_conv4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "b3_softmax (Activation)         (None, 1, 1, 2)      0           b3_conv4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "b4_softmax (Activation)         (None, 1, 1, 2)      0           b4_conv4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "b5_softmax (Activation)         (None, 1, 1, 2)      0           b5_conv4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "reshape_9 (Reshape)             (None, 3)            0           softmax[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "reshape_1 (Reshape)             (None, 2)            0           b2_softmax[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "reshape_3 (Reshape)             (None, 2)            0           b3_softmax[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "reshape_5 (Reshape)             (None, 2)            0           b4_softmax[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "reshape_7 (Reshape)             (None, 2)            0           b5_softmax[0][0]                 \n",
      "==================================================================================================\n",
      "Total params: 889,083\n",
      "Trainable params: 866,299\n",
      "Non-trainable params: 22,784\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "226\n",
      "72\n"
     ]
    }
   ],
   "source": [
    "print(len(model.trainable_variables))\n",
    "for layer in model.layers[:]:\n",
    "    if layer.name in freeze_layer_name:\n",
    "        layer.trainable =  False\n",
    "print(len(model.trainable_variables))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 474,
   "metadata": {},
   "outputs": [],
   "source": [
    "Adam = tf.keras.optimizers.Adam(learning_rate=0.0001)\n",
    "#sgd = tf.keras.optimizers.SGD(lr=0.0001, momentum=0.9, nesterov=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 475,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "def lr_scheduler(epoch):\n",
    "    if epoch % 10 == 0:\n",
    "        tf.keras.backend.set_value(model.optimizer.lr, model.optimizer.lr * 0.3)\n",
    "    return model.optimizer.lr\n",
    "change_lr = tf.keras.callbacks.LearningRateScheduler(lr_scheduler)\n",
    "'''\n",
    "\n",
    "reduce_lr = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_accuracy', factor=0.5,patience=3, min_lr=0.00001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 476,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=Adam,\n",
    "               loss=\"categorical_crossentropy\",\n",
    "               metrics=['accuracy'])\n",
    "# model.compile(optimizer=Adam,\n",
    "#               loss=[categorical_focal_loss(alpha=.25, gamma=2)],\n",
    "#               metrics=['accuracy'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 477,
   "metadata": {},
   "outputs": [],
   "source": [
    "#checkpoint_path = \"checkpoint_SE/cp.ckpt\"\n",
    "CKP_DIR_SAVE_CALLBACKS = './checkpoint/ckpt_QP22_mobile_tt/best_weight_branch_tt.h5'\n",
    "checkpoint_dir = os.path.dirname(CKP_DIR_SAVE_CALLBACKS)\n",
    "#checkpoint_dir = os.path.dirname(checkpoint_path)\n",
    "\n",
    "# Create a callback that saves the model's weights\n",
    "cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=CKP_DIR_SAVE_CALLBACKS,                                   \n",
    "                                                 save_weights_only=True,\n",
    "                                                 save_best_only=True,\n",
    "                                                 monitor='val_accuracy',\n",
    "                                                 verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 478,
   "metadata": {},
   "outputs": [],
   "source": [
    "#inputgenerator=generate_generator_multiple() \n",
    "#checkpoint_path = \"checkpoint_SE\"\n",
    "checkpoint_path = \"./checkpoint/ckpt_QP22_mobile/best_weight_branch1.h5\"\n",
    "#ckp = tf.train.latest_checkpoint(checkpoint_path)\n",
    "model.load_weights(checkpoint_path,by_name=True) \n",
    "\n",
    "checkpoint_path = \"./checkpoint/ckpt_QP22_mobile_tt/best_weight_branch_tt.h5\"\n",
    "\n",
    "model.load_weights(checkpoint_path,by_name=True) \n",
    "#checkpoint_path = \"ckpt_QP37_mobile_b4/best_weight_branch4.h5\"\n",
    "\n",
    "#model.load_weights(checkpoint_path,by_name=True) \n",
    "#testgenerator=generate_val_multiple()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "history = model.fit_generator(\n",
    "    train_data_gen,\n",
    "    #inputgenerator,\n",
    "    steps_per_epoch=total_train // batch_size,\n",
    "    epochs=epochs,\n",
    "    validation_data=val_data_gen,\n",
    "    #validation_data = testgenerator,\n",
    "    validation_steps=total_val // batch_size,\n",
    "    callbacks=[cp_callback,reduce_lr],\n",
    ")\n",
    "\n",
    "'''\n",
    "history = model.fit_generator(\n",
    "    TT_train_data_gen,\n",
    "    #inputgenerator,\n",
    "    steps_per_epoch=total_train_TT // batch_size,\n",
    "    epochs=epochs,\n",
    "    validation_data=TT_val_data_gen,\n",
    "    #validation_data = testgenerator,\n",
    "    validation_steps=total_valid_TT // batch_size,\n",
    "    callbacks=[cp_callback,reduce_lr],\n",
    "    \n",
    "    \n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as patches\n",
    "import math\n",
    "import os\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "filepath = \"D:/frame/\"\n",
    "seq_name = \"Johnny\"\n",
    "QP = \"QP37\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def frame_processing(filename,currframe = 0):\n",
    "    f = open(\"D:/frame/\"+filename+\"/\"+seq_name+'%d'%currframe+'.txt', \"r\")\n",
    "    rowlist = []\n",
    "    for line in f:\n",
    "        rowpix = line.split(\" \")\n",
    "        results = [int(i) for i in rowpix[:-1]]\n",
    "        rowlist.append(results)\n",
    "    img = np.asarray(rowlist)\n",
    "    img = img /4\n",
    "    return img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def frame_processing(filename,currframe = 0):\n",
    "    f = open(\"D:/frame/\"+filename+\"/\"+seq_name+'%d'%currframe+'.txt', \"r\")\n",
    "\n",
    "    rowlist = []\n",
    "    for line in f:\n",
    "        rowpix = line.split(\" \")\n",
    "        results = [int(i) for i in rowpix[:-1]]\n",
    "        rowlist.append(results)\n",
    "    img = np.asarray(rowlist)\n",
    "    img = img /4\n",
    "    return img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "origin frame:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2780df79780>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = frame_processing(seq_name,0)\n",
    "yuv_img = Image.fromarray(img.astype('uint8')).convert('YCbCr')\n",
    "\n",
    "print(\"origin frame:\")\n",
    "plt.imshow(yuv_img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def frame_padding(frame,CTU_size = 128):\n",
    "    width , height = frame.shape[1],frame.shape[0]\n",
    "    #print(width,height)\n",
    "    width_CTU_nums = math.ceil(width/128)\n",
    "    height_CTU_nums = math.ceil(height/128)\n",
    "    pad_frame = np.zeros((height_CTU_nums*128,width_CTU_nums*128))\n",
    "    for x in range(height):\n",
    "        for y in range(width):\n",
    "            pad_frame[x][y] = frame[x][y]\n",
    "    return pad_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "padding frame:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2780e6a7c18>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "yuv_img_padding = frame_padding(img)\n",
    "print(\"padding frame:\")\n",
    "yuv_img_padding = Image.fromarray(yuv_img_padding.astype('uint8')).convert('YCbCr')\n",
    "plt.imshow(yuv_img_padding)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# BackBone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "seq_list = [\"RaceHorses\",\n",
    "            \"BQSquare\",\"BlowingBubbles\",\"BasketballPass\",\n",
    "            ]\n",
    "QPList = [\"QP22\",\"QP27\",\"QP32\",\"QP37\"]\n",
    "\n",
    "def gen_range(yuv_img,yuv_img_padding,currframe = 0,index = 0,rd_mataining={}):\n",
    "    org_width,org_height = yuv_img.size\n",
    "    width , height = yuv_img_padding.size\n",
    "    \n",
    "    InFirstCTU = 0\n",
    "    with open(prediction_path+'frame'+str(currframe)+'.txt', 'w') as f:\n",
    "        for blocky in range(0, height, 128):\n",
    "            for blockx in range(0, width, 128):\n",
    "                for beginy in range(blocky, blocky+128, 32):\n",
    "                    for beginx in range(blockx, blockx+128, 32):\n",
    "                        if beginx > org_width or beginx + 32 > org_width or beginy > org_height or beginy + 32 > org_height:\n",
    "                            continue\n",
    "                        val = \"\"\n",
    "                        val += str(beginx)+\" \"+str(beginy)+\" \"+str(beginx+32)+\" \"+str(beginy+32)+\" \"\n",
    "                        item = pred_all[index]\n",
    "                        #print(item)\n",
    "                        #InFirstCTU+=1\n",
    "                        \n",
    "                        for ele in rd_mataining[item]:\n",
    "                            val += str(ele)+\" \"\n",
    "                            \n",
    "\n",
    "                        f.write(\"%s\\n\" % (val))\n",
    "                        index+=1\n",
    "                        InFirstCTU+=1\n",
    "    return index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for QP in QPList:\n",
    "    for seq_name in seq_list:\n",
    "        pred_all = []\n",
    "        checkpoint_path = \"./checkpoint/ckpt_\"+QP+\"_mobile/best_weight_branch1.h5\"\n",
    "        model.load_weights(checkpoint_path,by_name=True)       \n",
    "        test_path = \"/\"+QP+\"_block\"\n",
    "        idx = 0\n",
    "\n",
    "        for i in range(13):\n",
    "\n",
    "            predict_label = []\n",
    "            test_dir = \"D:/frame/\"+seq_name+test_path+\"/test\"+str(i)\n",
    "            print(test_dir)\n",
    "            test_image_generator = ImageDataGenerator(rescale = 1./255)\n",
    "            test_data_gen = test_image_generator.flow_from_directory(batch_size=1,\n",
    "                                                                          directory=test_dir,\n",
    "                                                                          target_size=(IMG_HEIGHT, IMG_WIDTH),\n",
    "                                                                          shuffle=False,\n",
    "                                                                          class_mode=None)\n",
    "\n",
    "            prediction=model.predict_generator(test_data_gen,verbose=1)\n",
    "\n",
    "\n",
    "\n",
    "            predict_label=np.argmax(prediction,axis=1)\n",
    "            for label in predict_label:\n",
    "                pred_all.append(label)\n",
    "\n",
    "                \n",
    "        rd_mataining = {0:[2,2,3,0],1:[2,4,4,0],2:[3,4,4,2]} #(5-6,7-8,9-10)\n",
    "        #prediction_path = \"D:/frame/\"+seq_name+\"/Prediction/\"+QP+\"/\"\n",
    "        prediction_path = \"D:/frame/\"+seq_name+\"/re_pred/\"+QP+\"/\"\n",
    "        index = 0\n",
    "        for i in range(13):\n",
    "            img = frame_processing(seq_name,i)\n",
    "            yuv_img = Image.fromarray(img.astype('uint8')).convert('YCbCr')\n",
    "            yuv_img_padding = frame_padding(img)\n",
    "            yuv_img_padding = Image.fromarray(yuv_img_padding.astype('uint8')).convert('YCbCr')\n",
    "            index = gen_range(yuv_img,yuv_img_padding,i,index,rd_mataining)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# BackBone & Three Branch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "seq_list = [\"Tango2\",\"FoodMarket4\",\"Campfire\",\"CatRobot1\",\"DaylightRoad2\",\"ParkRunning3\",\"MarketPlace\",\"RitualDance\",\n",
    "            \"Cactus\",\"BasketballDrive\",\"BQTerrace\",\"RaceHorsesC\",\"BQMall\",\"PartyScene\",\"BasketballDrill\",\"RaceHorses\",\n",
    "            \"BQSquare\",\"BlowingBubbles\",\"BasketballPass\",\"FourPeople\",\"Johnny\",\"KristenAndSara\"\n",
    "            ]\n",
    "#seq_list = [\"Tango2\"]\n",
    "# seq_list = [\"Cactus\"]\n",
    "QPList = [\"QP37\"]\n",
    "#QPList = [\"QP22\",\"QP27\",\"QP32\",\"QP37\"]\n",
    "\n",
    "def gen_range(yuv_img,yuv_img_padding,currframe = 0,index = 0,rd_mataining={}):\n",
    "    org_width,org_height = yuv_img.size\n",
    "    width , height = yuv_img_padding.size\n",
    "    \n",
    "    InFirstCTU = 0\n",
    "    with open(prediction_path+'frame'+str(currframe)+'.txt', 'w') as f:\n",
    "        for blocky in range(0, height, 128):\n",
    "            for blockx in range(0, width, 128):\n",
    "                for beginy in range(blocky, blocky+128, 32):\n",
    "                    for beginx in range(blockx, blockx+128, 32):\n",
    "                        if beginx > org_width or beginx + 32 > org_width or beginy > org_height or beginy + 32 > org_height:\n",
    "                            continue\n",
    "                        val = \"\"\n",
    "                        val += str(beginx)+\" \"+str(beginy)+\" \"+str(beginx+32)+\" \"+str(beginy+32)+\" \"\n",
    "                        item = pred_all[index]\n",
    "                        #print(item)\n",
    "                        #InFirstCTU+=1\n",
    "                        if index in candidate_idx:\n",
    "\n",
    "                            #print(candidate_idx)\n",
    "                            qtitem = pred_conf_label[index]\n",
    "\n",
    "                            if qtitem == 0:\n",
    "                                pred_all[index] = 3\n",
    "                                #print('in')\n",
    "                                for ele in rd_mataining[3]:\n",
    "                                    val += str(ele)+\" \"\n",
    "\n",
    "                            else:\n",
    "                                pred_all[index] = 4\n",
    "                                for ele in rd_mataining[4]:\n",
    "                                    val += str(ele)+\" \"\n",
    "                        elif index in candidate_idx_mid:\n",
    "\n",
    "                            qtitem = pred_conf_label_mid[index]\n",
    "\n",
    "                            if qtitem == 0:\n",
    "                                pred_all[index] = 5\n",
    "                                #print('in')\n",
    "                                for ele in rd_mataining[5]:\n",
    "                                    val += str(ele)+\" \"\n",
    "                                \n",
    "\n",
    "                            else:\n",
    "                                pred_all[index] = 6\n",
    "                                #print('in')\n",
    "                                for ele in rd_mataining[6]:\n",
    "                                    val += str(ele)+\" \"\n",
    "                                    \n",
    "                        elif index in candidate_idx_QT12:\n",
    "\n",
    "                            qtitem = pred_conf_label_QT12[index]\n",
    "\n",
    "                            if qtitem == 0:\n",
    "                                pred_all[index] = 7\n",
    "                                #print('in')\n",
    "                                for ele in rd_mataining[7]:\n",
    "                                    val += str(ele)+\" \"\n",
    "                                \n",
    "\n",
    "                            else:\n",
    "                                pred_all[index] = 8\n",
    "                                #print('in')\n",
    "                                for ele in rd_mataining[8]:\n",
    "                                    val += str(ele)+\" \"\n",
    "                        else:\n",
    "                            for ele in rd_mataining[item]:\n",
    "                                val += str(ele)+\" \"\n",
    "                        #print(pred_all_tt)\n",
    "                        if pred_all_tt[index] == 0:\n",
    "                            val += \"1\"\n",
    "                        else :\n",
    "                            val += \"0\"\n",
    "                        f.write(\"%s\\n\" % (val))\n",
    "                        index+=1\n",
    "                        InFirstCTU+=1\n",
    "    return index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for QP in QPList:\n",
    "    for seq_name in seq_list:\n",
    "        pred_all = []\n",
    "        pred_all_tt = []\n",
    "        checkpoint_path = \"./checkpoint/ckpt_\"+QP+\"_mobile/best_weight_branch1.h5\"\n",
    "        model.load_weights(checkpoint_path,by_name=True) \n",
    "        checkpoint_path = \"./checkpoint/ckpt_\"+QP+\"_mobile_b2/best_weight_branch2.h5\"\n",
    "        model.load_weights(checkpoint_path,by_name=True) \n",
    "        checkpoint_path = \"./checkpoint/ckpt_\"+QP+\"_mobile_b3/best_weight_branch3.h5\"\n",
    "        model.load_weights(checkpoint_path,by_name=True) \n",
    "        checkpoint_path = \"./checkpoint/ckpt_\"+QP+\"_mobile_b4/best_weight_branch4.h5\"\n",
    "        model.load_weights(checkpoint_path,by_name=True) \n",
    "        checkpoint_path = \"./checkpoint/ckpt_\"+QP+\"_mobile_tt/best_weight_branch_tt.h5\"\n",
    "        model.load_weights(checkpoint_path,by_name=True) \n",
    "\n",
    "        candidate_idx = []\n",
    "        pred_conf_label = []\n",
    "        candidate_idx_mid = []\n",
    "        pred_conf_label_mid = []\n",
    "        candidate_idx_QT12 = []\n",
    "        pred_conf_label_QT12 = []\n",
    "        Threshold = 0.6\n",
    "        Threshold_QT12 = 0.5\n",
    "        Threshold_QT78 = 0.5\n",
    "        \n",
    "        test_path = \"/\"+QP+\"_block\"\n",
    "        idx = 0\n",
    "        idx_mid = 0\n",
    "        idx_QT12 = 0\n",
    "        for i in range(13):\n",
    "\n",
    "            predict_label = []\n",
    "            test_dir = \"D:/frame/\"+seq_name+test_path+\"/test\"+str(i)\n",
    "            print(test_dir)\n",
    "            test_image_generator = ImageDataGenerator(rescale = 1./255)\n",
    "            test_data_gen = test_image_generator.flow_from_directory(batch_size=1,\n",
    "                                                                          directory=test_dir,\n",
    "                                                                          target_size=(IMG_HEIGHT, IMG_WIDTH),\n",
    "                                                                          shuffle=False,\n",
    "                                                                          class_mode=None)\n",
    "\n",
    "            prediction,pred_QT,pred_QT_mid,pred_QT12,pred_tt=model.predict_generator(test_data_gen,verbose=1)\n",
    "\n",
    "\n",
    "\n",
    "            predict_label=np.argmax(prediction,axis=1)\n",
    "            ###\n",
    "            predict_label_tt=np.argmax(pred_tt,axis=1)\n",
    "            for label in predict_label_tt:\n",
    "                pred_all_tt.append(label)\n",
    "            #print(pred_all_tt)\n",
    "            ###\n",
    "            for label in predict_label:\n",
    "                pred_all.append(label)\n",
    "\n",
    "            pred_QT_label = np.argmax(pred_QT, axis=1)\n",
    "            pred_QT_mid_label = np.argmax(pred_QT_mid, axis=1)#\n",
    "            pred_QT12_label = np.argmax(pred_QT12, axis=1)#\n",
    "            #print(pred_QT12_label)\n",
    "            ################  Depth910 QT 23 branch  ###############\n",
    "            for label in pred_QT_label:\n",
    "                pred_conf_label.append(label)\n",
    "            for pred in pred_QT[:]:\n",
    "                if(np.amax(pred, axis=0) >= Threshold and pred_all[idx] == 2):\n",
    "                    candidate_idx.append(idx)\n",
    "                idx+=1\n",
    "            ################  Depth78 QT 23 branch  ###############\n",
    "            for label in pred_QT_mid_label:\n",
    "                pred_conf_label_mid.append(label)\n",
    "            for pred in pred_QT_mid[:]:\n",
    "                if(np.amax(pred, axis=0) >= Threshold_QT78 and pred_all[idx_mid] == 1):\n",
    "                    candidate_idx_mid.append(idx_mid)\n",
    "                idx_mid+=1\n",
    "             ################   QT 12 branch  ###############\n",
    "            for label in pred_QT12_label:  \n",
    "                pred_conf_label_QT12.append(label)\n",
    "            for pred in pred_QT12[:]:\n",
    "                if(np.amax(pred, axis=0) >= Threshold_QT12 and pred_all[idx_QT12] == 0):\n",
    "                    candidate_idx_QT12.append(idx_QT12)\n",
    "                idx_QT12+=1\n",
    "                \n",
    "        rd_mataining = {0:[2,2,3,0],1:[2,4,4,0],2:[3,4,4,2],3:[2,5,2,5],4:[3,4,3,4],5:[2,4,2,4],6:[3,2,3,2],7:[2,0,2,0],8:[2,2,3,0]} #(5-6,7-8,9-10)\n",
    "        #prediction_path = \"D:/frame/\"+seq_name+\"/Prediction/\"+QP+\"/\"\n",
    "        prediction_path = \"D:/frame/\"+seq_name+\"/Prediction/\"+QP+\"/\"\n",
    "        index = 0\n",
    "        for i in range(13):\n",
    "            img = frame_processing(seq_name,i)\n",
    "            yuv_img = Image.fromarray(img.astype('uint8')).convert('YCbCr')\n",
    "            yuv_img_padding = frame_padding(img)\n",
    "            yuv_img_padding = Image.fromarray(yuv_img_padding.astype('uint8')).convert('YCbCr')\n",
    "            index = gen_range(yuv_img,yuv_img_padding,i,index,rd_mataining)"
   ]
  }
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